Over the last decade, the term ‘digital transformation’ has become prevalent across a wide variety of organisations. It refers to converting existing manual processes to create a more efficient and agile business environment. In 2018, >70% of organisations were reported as having a digital strategy or working to implement one (1). Johnson Matthey has established both key innovation programmes and the Digital Johnson Matthey programme to bridge between IT and the business to deliver ‘digital spearhead’ initiatives to meet this goal.

Digital transformation initiatives are part of a global shift towards so-called Industry 4.0 programmes. Evolving from established industrialisation practices, this future way of working builds upon the foundations of streamlined value-chain operations and automation by embedding data, modern smart technology, artificial intelligence and robotics in a seamless manner.

In order to stay competitive, organisations need to improve their internal processes to deliver faster innovation across research and development (R&D) and manufacturing, while accommodating the shifting needs of customers and macroeconomic factors. Additionally, companies are increasing external collaborations with networks of partnerships and innovation centres, to access new technology and capabilities that complement in-house competencies (2). A modern digital infrastructure can facilitate this by providing effective exchange of information alongside a culture of continuous improvement, with an emphasis on operational agility and experimentation to drive the desired outcomes.

Expected benefits of recognising that data is an asset are operational efficiency gains, with the ability to improve product quality and reduce development time and cost. This directly yields an improved competitive position in the marketplace. Moreover, there may also be opportunities to develop new revenue streams by aligning physical product offerings with ancillary software optimisation applications. The Johnson Matthey Levo^TM application for plant optimisation is a good example of this.

The global impact of COVID-19 was widespread and transformative in its own right, as organisations rapidly adapted. With remote working and a need for business continuity, companies accelerated digitisation of systems supporting all manner of business functions. The response to the pandemic and mitigating actions to ensure business continuity have helped to speed the adoption of digital technologies. Many of these changes are embedded and expected to be long lasting. The value of the digital strategic initiative is recognised: 53% of companies plan to cut or defer capital investments because of COVID-19, but just 9% will make cuts in digital transformation efforts (3).

Data is the new digital fuel that is the heart of the Industry 4.0 initiative. Both legacy and current research data are used to create and power the artificial intelligence algorithms and modelling approaches that lead to break-through product innovations.

Historically, attempts at mining legacy data were challenging because data was often in disparate systems and formats, which took time to find, and transcribing information from paper records was cumbersome and error prone. Across many organisations, there has often been fragmentation of ownership of data across disparate groups, as well as segmentation across the organisation, creating barriers to shared information.

The industry-recognised approach is now for data to adhere to Findable, Accessible, Interoperable and Reusable (FAIR) guidelines. By moving to electronic records and systems that allow for structured data capture, i.e., with well-defined metadata and results fields, data scientists and modellers will have near real-time access to a wealth of research and process engineering records.

As technology plays a more pivotal and crucial role in creating an agile business environment, organisations need to recognise that embracing digital tools and analytics helps to unlock the full potential of data. This in itself requires a shift in mindset, necessitating behavioural changes and learnings to manage data more effectively on a day-to-day basis. The community needs to store data in a meaningful manner, to open data repositories and to apply data governance and agreed practices that make the data accessible and clear for other people to use. This task is not insignificant, and conscious effort is required to align to this new way of working and for people to recognise the opportunities that their data presents.

The transformation process is essentially facilitating communication and exchange between stakeholders i.e., between different research, analytical, development and manufacturing departments, to those that ultimately service the external customer. As an organisation transitions from paper to spreadsheets to smart applications for managing these interactions, there is an opportunity to reconsider how processes are performed and how information is communicated, using digital technology.

As organisations work to overcome obstacles and drive operational efficiencies towards improved competitiveness, it is important to recognise that a digital transformation initiative cannot simply be solved by introducing a suite of new tools and applications. In a 2016 survey, 87% of companies thought that digital would disrupt their industry, while only 44% felt prepared for these potential digital changes, and little has changed since then (4, 5). As such, there needs to be a company-wide shift in thinking and process, alongside training and support. With CEO and senior management encouragement, the culture of change across the entire organisation needs to be prioritised. Importantly, there is also a converse ‘bottom up’ alignment, with engagement from end-users who recognise inefficiencies in current practices and who are enthusiastic and contribute ideas about new ways of working.

The challenges of creating a world that is cleaner and healthier, today and for future generations, will only be solved by engaging with disruptive innovation that is driven by digital transformation. As a result, organisations are rapidly developing, adjusting or accelerating strategies to provide the required technical and business agility. This extends from how their employees work and collaborate to how they engage with partners, suppliers and customers. The technology disruptors of today will help make the workplace a data-driven organisation, leveraging technology and culture change to drive business strategy in ways that help promote growth, spur innovation, reduce costs, streamline operations and create satisfied, loyal customers.

Domestic and commercial vehicles are leading sources of global pollution, with vehicle emissions risking the health of communities near roads (1). Fine and ultrafine particulate matter, oxides of nitrogen, hydrocarbons and carbon monoxide are key road traffic pollutants that are associated with adverse health effects (2). Catalytic converters have been used since the 1970s to reduce the emission of these pollutants by catalysing their reaction into less-toxic substances, typically carbon dioxide, nitrogen and water (3). However, current catalytic converters are not 100% efficient in their reactions of pollutants and moreover have variable efficiency at different operating temperatures.

This work uses machine learning modelling to analyse current catalytic converter performance and identify which future experimental tests would add most value to the ongoing development of improved catalytic converters. Previous work using machine learning in the catalysis domain has tended to focus on either augmenting quantum mechanical models of catalyst function (4–8), screening potential new catalysts (7–11), or predicting properties from carefully-selected chemical descriptors of catalysts (6, 8, 12–14). In contrast, in this work we focus on modelling catalyst properties from the formulation ingredients and processing variables of the catalyst. The ingredients and processing conditions of samples are easily accessible during the development process, lowering the barrier to application of machine learning in active development projects. In the following section we discuss the project objectives, detail the machine learning methodology used and the results it delivers, before looking forward to potential future applications of machine learning for materials science in the automotive field and beyond.

We collated data on 612 catalytic converter test sets that have been manufactured and experimentally tested by Johnson Matthey as part of an ongoing catalyst development project. The data contained information on the formulation used for the catalysts, including amounts and properties of 34 ingredients; 10 test parameters describing the testing process for each catalyst; and 16 experimentally measured properties for each catalyst including target gas conversions and selectivities. These output properties consisted of eight sets of tests, with each test run at both a high (approx. 500°C) and low (approx. 225°C) temperature on different samples of the same catalyst formulation. Each experimental property was reported as a steady-state average over 50–100 s of gas stream.

Using this data, we aimed to build understanding of the performance of this class of catalyst, using a machine learning model trained on the data to extract information on which input features of the formulation and processing parameters have most impact on the performance. Using this model, we then designed catalysts that offer both high performance and also add value to the machine learning model, which once made and measured can be added to the training dataset to enable more accurate modelling of high performance catalysts.

To model the catalyst data we used the Alchemite^TM multi-target machine learning platform. This method is described in detail in the literature (15–17), but in brief consists of iteratively generating predictions for all data series, both input and output, and using these predictions to impute missing data on the input side, before the final iteration of predictions are reported as the predictions for the output series. This method is designed to handle sparse input data, as was found in this work where up to 10% of the catalysts were missing information on each of the input properties. As the method is multi-target, generating predictions for all output properties simultaneously, we trained a model to predict all 16 experimentally measured properties at once. Alchemite^TM also generates estimates of the uncertainty in each prediction, which is vital to prioritise suggestions for future experiments that are most likely to achieve specified objectives. To test the performance of the model, data on 61 catalysts (10% of the data) was randomly held back; the model was trained on data for the remaining 551 catalysts. Hyperparameters of the model were optimised using Bayesian Tree of Parzen Estimators via five-fold cross-validation within the training set only (17, 18).

To test the performance of the model we simultaneously predicted all 16 output properties for each of the 61 held-back catalysts and measured the coefficient of determination R², for each output property. The coefficient of determination is defined as Equation (i):

(i)

where i indexes each catalyst in the validation set; y_i are the true experimental values, with mean ȳ; and f_i are the model predictions. A value of 1 indicates a perfect fit between model and experimental values; a value of 0 indicates a fit that is no better than random chance; and negative values indicate predictions that are worse than random. The performance of the model is shown in light blue in Figure 1. The median R² across all the output properties is 0.71, indicating highly successful predictive accuracy. In Figure 1 we also compare to two robust standard machine learning approaches: support vector regression with radial basis function kernel and K nearest neighbours with 20 neighbours, implemented in scikit-learn (19), which were trained on a mean-imputed version of the ingredient and test parameter data and achieve baseline median R² values of 0.52 and 0.49 respectively.

Fig. 1.

We observed that the predictions for Property 6, at both high and low temperatures, were poor: we identified that although changes in Property 6 are observable, a key physical mechanism directly influencing the value of Property 6 is driven by a chemical species not easily measurable by any analytical method and so is not fully captured in the dataset used to train the models. This explains the poor performance of the models in this aspect. The addition of (perhaps heuristic) descriptors to capture the physical mechanism may improve the modelling performance (14), but at the cost of increasing the barrier to usage of the method compared to taking only ingredients and processes as input.

Because the experimental tests on the catalysts are each repeated, run first at high temperature and then at low temperature, these results can be correlated so there is the possibility of increasing the efficiency of the testing process by using machine learning to replace one of the rounds of testing. To validate this, we trained a machine learning model that took as inputs the formulation ingredients and test parameters as well as the experimentally measured results on all eight tests at high temperature, and predicted the results of the eight tests at low temperature. This order (using high temperature results as input to predict low temperature results) was selected to align with the current testing methodology.

The improved performance by using the high temperature measurements to help predict the low temperature performance is exemplified in dark blue in Figure 1. For five of the eight experimental properties the accuracy significantly increased (increase in R² of more than 0.1), and for Properties 1, 2 and 3 the resulting accuracy, with R²>0.95, is effectively equivalent to the experimental uncertainty in the measurement. For these three properties in particular, machine learning predictions could reliably replace experimental measurements, offering a saving in the time and effort required to run the experimental tests on new catalysts. The three experimental properties that were not improved by using the high temperature measurements are all related to the same target gas’ conversion rates, although it is not clear why these properties are not improved by access to increased experimental data. These three experimental properties are less commercially important than Property 1, which is the property with most commercial relevance.

Now that we have confirmed the accuracy of the model we are well-positioned to extract actionable insights. Therefore, we first analyse the relationships that the model identified between inputs and outputs. To do so we examined which input features are used by the model when making predictions for each of the output properties, by evaluating the overall relative weights assigned to each input feature by the trained model, i.e. what fraction of the model prediction for each output is attributable to each input feature, on average across the whole model. This is calculated using the information gain attributable to each input feature (20). The results are summarised in Figure 2, separately for the model trained to predict both high and low temperature properties and the model trained to predict low temperature properties only. Averaging across each of the output properties, we find that for the high and low temperature model the test parameters and formulation ingredients are utilised in the proportion 0.59:1, and for the low temperature only model the test parameters, formulation ingredients and experimental high temperature measurements are utilised in the proportion 0.60:1:1.19. The consistent ratio of 0.6:1 in utilisation of the test parameters and formulation ingredients between the two models indicates that the high temperature experimental measurements (especially Properties 1, 2 and 3) are adding distinct information to the model that it was not capable of identifying from either the test parameters or formulation ingredients.

Fig. 2.

The key operational insight derived from this analysis was that although the formulation ingredients provide important information for the simultaneous modelling of the high and low temperature results, the variation in the test parameters also provides a key contribution. Historically the test parameters have been controlled within specification ranges but the impact of variation within these ranges has not been considered. These results show that the test parameters have an impact on the resulting properties and that control and understanding of these parameters improves the value of the data.

With increased understanding of the importance of the test parameters for measured catalyst performance, we used the machine learning model to design catalyst formulations. For performance targets, we focussed on the most commercially important property (Property 1), aiming to maximise its value at both high and low temperatures, and for that value to be stable with temperature. Although Property 1 is the most commercially important property, the values of the other properties are also required for product success.

As well as looking for the formulations that would be most likely to succeed against these performance targets (‘exploitation’ of the model) we also searched for formulations that, when measured, would increase the model’s understanding of the formulation landscape and so improve future rounds of predictive modelling and formulation design (‘exploration’ of the model), as well as a balanced mix of the two objectives. We used a Bayesian search of the formulation space using Tree of Parzen Estimators (18) built into the Alchemite^TM platform, taking as the cost function the probability of simultaneously achieving all the performance targets, including a contribution from the uncertainty in each formulation’s predicted performance calculated as standard errors across the Alchemite^TM platform’s internal ensemble of sub-models (21). This cost function is the commercially relevant metric to aim to propose successful and useful new formulations. Exploitation-focused suggestions prioritise formulations with high probability of success, while exploration-focused suggestions prioritise formulations whose predictions are currently uncertain and will also help improve predictions over a wide range of formulation space.

A two-dimensional Uniform Manifold Approximation and Projection (UMAP) embedding (22) of the formulations is shown in Figure 3. The dark blue points show the historic experimental results, with more opaque points having higher performance against Property 1 and more transparent points having lower performance. We observe that there are several clusters of dissimilar formulations that had previously been measured, but that most of the formulations were relatively similar and are clustered in the centre of the plot (this clustering analysis being a key strength of the UMAP approach). Figure 3 also shows the formulations proposed by the machine learning approach, labelled by whether they are focused on exploration, exploitation or a balanced mixture. We observe that, as expected, the exploitation-focused suggestions are clustered more tightly at the centre of the plot, demonstrating that they are attempting to exploit a class of formulations with a high probability (up to 60%) of achieving all of the design targets simultaneously. In contrast, the exploration-focused suggestions are more varied, focusing particularly on gaps in the existing coverage of the formulation space where additional information will improve the model. The balanced suggestions show aspects of both behaviours. A subset of the formulations suggested by the machine learning, including samples from the exploration, exploitation and balanced suggestions, are currently undergoing experimental validation.

Fig. 3.

In this work we have shown how machine learning analysis of catalyst formulations enables new insights into the factors that affect catalyst performance, including particularly that the test parameters more strongly impact the eventual performance than was initially anticipated: this will have operational significance for the future of this product development. We have also shown how the use of a machine learning platform, rather than a single predictive tool, can enable full design workflows, including prioritising exploration of the formulation space or exploitation of a model to achieve high product performance, accelerating the design process by enabling a holistic view of the formulation opportunities. Future progress in this project could focus on achieving multiple target properties simultaneously, beyond only Property 1, or utilising the accurate predictions of low temperature measurements based on experimental high temperature measurements to halve the amount of experimental effort required when screening new formulations.

The machine learning approach here is applicable beyond catalytic converters, including the design of metal alloys (15, 23), batteries (24), and pharmaceutical drugs (21). A machine learning platform that can carry out the full cycle of formulation development, handling sparse real-world experimental data, building predictive models and proposing and interpreting new formulation designs adds value in each of these areas, with reduced barrier to entry by working directly on the composition and processing variables immediately accessible to project scientists.

FAIR data principles have been held as the gold standard for ensuring data across the sciences and across individual institutions is generated and kept in as sustainable a way as possible (1). FAIR data principles unlock powerful ‘data lake’ workflows that allow for multiple interactions, machine learning and deep insight to be gained, adding value to already collected data (2). Reports and peer reviewed publications are needed to share knowledge with others at both an inter- and intra-institution level.

One nemesis to this approach is the use of proprietary software and proprietary data standards. It has been suggested that all research software should be free open source software (FOSS) and that closed source software should be the exception (3). The use of FOSS and open source hardware has been shown to offer flexibility and insight in a range of practical applications within chemical R&D (4–6).

A wealth of new software is available every year including productivity tools, document management, data analysis suites and code produced via individuals or research groups. One recent report showed that ~51,000 publications in the life sciences had 25,900 unique pieces of software cited (7). In addition to the wealth of new software offerings humans are keenly biased towards additive problem solving (8). Adding to an existing system rather than taking away in order to solve a problem is seen across sectors, job roles and in the digital tools used to enable science. An exemplar of this type of approach in software was seen with the introduction of the ribbon into Microsoft Office. Those more experienced with the software were more likely to be dissatisfied and impeded by the addition of the ribbon into the Office suite (9). Frustration stemming from unclear error messages, poor wording and lack of training lead to a loss of as much as 40% of a user’s time trying to solve software related issues (10).

As we train the next generation of scientists, and during the course of professional development, it is imperative that individuals reflect on and take control of the digital tools used to plan, conduct and share work. Frustration can be avoided if the tool being used is understood. Ideally, any skills learned during any part of an individual’s scientific career should be transferable. This is not possible if proprietary software solutions are used as there is no guarantee the software will be available at a new role either due to funding, dropped support or incompatibility with other systems.

One part of the solution to this, as demonstrated clearly by software projects like GNU/Linux (referred to herein as Linux), is the use of open source plain file formats like text files. Text files are human and computer readable, have demonstrable longevity and, crucially, are free and open source. Coupling this with tools that allow users to build, maintain and deploy their own solutions could resolve many of the frustrations seen with modern computer use.

Herein a demonstration of a workflow using a single tool, working with just text files, that can be used to radically change the workflow of a modern, flexible and agile scientist. The key benefits are increased productivity, return on investment, cost and environment, health and safety via improved ergonomics. In this viewpoint it will be demonstrated that such a solution exists and how it can be used in the context of corporate R&D.

Figure 1 shows two simplified workflows. Figure 1(a) shows the current state for many scientists. Each box in this flow represents a separate piece of software. These often have different shortcut keys, require many open programs and limit the user in terms of customisability and automatic flows. Each box may represent a different piece of software with separate associated upkeep costs, adding to both R&D expenditure and cost to monitor and ensure compliance with licenses. Figure 1(b) shows one possible solution where a single software solution replaces all the programs in a digital workflow. This workflow is possible with the open source and free program: Emacs.

Fig. 1.

Emacs is a fully programmable and extensible text editor. It is used widely in the IT and programming fields. Originally developed in the 1970s, the version used today (GNU Emacs – referred to herein as Emacs) was developed in the 1980s by Richard Stallman. It may seem retrograde that a decades old software solution can compete with newer offerings, but its longevity speaks to its utility. Emacs has been maintained and updated throughout this period with versions available across Windows, macOS and Linux.

Out of the box Emacs is a blank canvas. The decades of use mean that many contributors have written, maintained and updated a large number of packages that can be downloaded and used for free. These packages are completely user customisable and self-documenting. Emacs allows the user to employ these packages to build what is needed from the ground up. The below examples demonstrate how this approach can be used in a range of tasks in corporate R&D. This was built and personalised in-house with speed and ease of use being key. By building this tool from the ground up there is no bloat or incompatibilities that come with other, long lived, commercial solutions.

Figure 2(a) and 2(b) shows the software loaded in either its unmodified form or after the application of one of the many distributions, in this case Spacemacs. These distributions come preconfigured for ease of use and with many quality of life features. It is possible for a user to use one of these or to build their own version.

Fig. 2.

Because the below use cases can be achieved from within one piece of software, productivity and focus can be retained with the use of suite-wide shortcuts and hot keys. This reduces the possibility of fragmented work which can reduce productivity (11). Emacs is also fully controllable from the keyboard, again improving speed, productivity and ergonomics.

Org-mode is a major mode (a set of instructions for how certain files should be handled) for Emacs which was developed in 2003 by astrophysicist Carsten Dominik. Initially as a way to organise Dominik’s work, it has grown into a full suite. Allowing for everything from ‘todo’ task management to note taking and scientific manuscript preparation.

Importantly, it allows for a single document to contain data, working code and prose (12). Org mode has several minor modes (options that can be turned on or off) which can unlock advanced features impossible with other free or commercial solutions. These will be discussed in the following sections.

Data generation does not happen in a vacuum. A scientist’s work day includes ‘scientific overheads’ that can dramatically lower the time spent by an individual on the act of conducting high quality science (13). Indeed only ~40% of young researchers’ time in academia is spent on research, with the majority of the remaining time spent on writing and administration (14).

This is represented pictographically as the first set of software in Figure 1(a). This can be thought of as everything up to the act of experimentation along with all the administration tasks associated with modern knowledge work. Emails, meetings and conferences all add to the overhead workers face. The following section is not an exclusive list of what can be done but aims to demonstrate a few case studies of how Emacs can remove the burden of scientific overheads by consolidation of tasks with Emacs and Org-mode.

The act of producing, reviewing and executing a plan is an essential component of problem solving (15). Time management behaviours improve job satisfaction and health while negatively impacting stress (16). Org-mode allows for easy task management and planning from within the Emacs environment.

By setting up ‘Org-capture’, a package that works with Org-mode, todos can be captured and stored centrally from anywhere within Emacs. This makes capturing and recording tasks without interruption to flow trivial. Agendas and todo lists can be automatically populated from multiple sources (for example, reading list, meeting notes, project files). Importantly this approach works well with systems like ‘getting things done’ while staying flexible enough to allow for individual customisation (17). Examples of todo management as well as automatically generated agenda views can be found in Figure 3(a) and 3(b) respectively.

Fig. 3.

Additionally other tedious tasks can be automated. The use of tools like ‘Yasnippet’ allow for chunks of text to be stored and pasted into a document with only a few key presses. The production of meeting notes, for example, can be sped up by producing a template which can be imported. These can be exported via a .tex file and rendered into a PDF using LaTeX. This may seem arduous but, once set up, this is completely automated.

Macros can also be recorded and called when needed. If any task is done repeatedly then tools with Emacs can be used to automate that process. This reduction of overheads frees up a scientist to allow them to do what generates value for companies and academic institutions alike.

In a world where scientists are not just expected to produce data but be fully fledged knowledge workers, tools like this are invaluable. Their flexibility and utility can be tailored to the user’s workflow, enabling high productivity work to be conducted.

The act of collecting, reading and making notes on reference materials is a key aspect of scientific work. Importantly any possible solution to digitalise this should allow for citations to be placed within documents as well as easy access to referencing styles. This is possible with commercial solutions and even some open source options. Where an Emacs workflow outshines all is that the reference manager, note taking, citation tools and writing program are all one.

Packages like ‘Org-ref’ allow for import of PDFs from digital object identifiers (DOIs) allowing for fast import and conversion into a defined bibtex file (the plain text file used by LaTeX to generate citations). Notes can be accessed quickly using a package like ‘Interleave’ or ‘Org-noter’ which allows for automated note taking during the reading of a document, Figure 4.

Fig. 4.

Linking of notes and PDFs is extremely powerful and a rarity in the reference manager space. Due to the notes being in plain text they are also searchable unlike PDF highlighting or other, non-text or paper based, approaches.

One of the benefits of multidisciplinary teams is learning about best practices outside of one’s field. One concept that has taken hold in the computer science world is that of literate programming. Literate programming is the idea that written code should not just tell a computer what to do but that it is imperative that the code also informs a human about what is running (18).

This approach should be common to scientists. The aim of written reports, manuscripts and presentations is to display complex data and analysis in an easy to understand form for humans. The problem, as we approach more complex analysis, is that: (a) the analysis is split from the final report or manuscript which leads to loss of reproducibility; or (b) that the analysis is hidden in proprietary software that does not conform to FAIR principles nor the longevity principles a large corporate or academic institution may expect.

Org-mode, by utilising ‘Org-babel’, allows for chunks of code to be written and executed from within a single document. Variables can be extracted from these code blocks and then embedded in the text or fed into other code blocks. There are clear parallels between this type of approach and that of the IPython/Jupyter notebooks. These notebooks offer similar advantages in combining prose and code, allowing for reproducibility in data analytics. Both Emacs Org-mode and IPython/Jupyter notebooks offer parallelisation as a feature within the language. These notebooks do, however, suffer from the same issues described above as they form part of a fragmented software solution. As will be described below, they also lack the ability to embed analysis to a final manuscript.

Plotting can be done in the same way with direct output to a number of image formats that can, in turn, be embedded into the Org file. If one simply wants a way to record one’s work in an easy to follow format which is completely human readable then Org-mode makes that a simple task. Where the power of this approach becomes evident is when this is linked with manuscript or report production.

Org files are human readable with any text editor but Emacs unlocks many ways to quickly access the myriad of features not available outside Emacs. Importantly Org files can be exported in a range of formats including PDFs, markdown and open document formats. This manuscript was prepared as a Org file which was automatically processed into a .tex file and rendered into a PDF. Tools like ‘Writeroom-mode’ format documents to allow for a distraction-free writing experience, Figure 5.

Fig. 5.

When it comes to reports and manuscripts written in Emacs and Org-mode it is trivial to produce literate documents. Data and analysis can all be included within the manuscript which is also machine accessible. This works well with FAIR principles allowing for a human readable document to also act as metadata and a repository for computer readable data. To demonstrate this Figure 6(a) is a plot rendered by Python code embedded in this document. The values have been calculated from data within the file. The code snippet for this can be seen in Figure 6(b). If any changes are made to the analysis or the data, the plot is updated. This means that a single Org file can be provided and all data and analytics can be reproduced. It also makes the process of data analytics and report writing much easier. Any changes to the analysis will be updated in the text, either via plots or by embedded variables. This reduces the cognitive load associated with making requested changes, either during the peer review cycle or due to feedback from colleagues.

Fig. 6.

Previous reports have demonstrated how experimental data can be embedded into PDFs produced from Emacs allowing for a manuscript or report to contain all the data reported (19). The benefits of this are clear for both scientific integrity and rigour but also as a way to ensure a report or manuscript can be understood fully if an employee were to leave an institution, retaining the value of that work indefinitely.

Emacs has a reputation of being difficult to learn and this should not be ignored. Emacs has a learning curve however this can be as steep or as shallow as the user needs. Emacs distributions like ‘Spacemacs’ or ‘Doom Emacs’ allow for mnemonics key bindings and other quality of life features. Vanilla Emacs has many of the graphical user interface aspects you would expect, such as menus, which allows for most of the functionality to be explored. Becoming proficient takes time however this comes slowly as utility is unlocked. As summarised by John Kitchin:

“Scientific publishing is a career-long activity, and one should not shy away from learning a tool that can have an impact over this time scale.” (19)

While this still holds true, the author feels it is imperative to add the same is true of all aspects of a scientist’s workflow including productivity, reference management and data analytics.

Additionally, despite best efforts, all aspects of an Emacs workflow may not be possible. Email is possible within Emacs. However due to some institutions’ policies, such as Azure Information Protection, it may not be possible to set up due to issues with accessing confidential information without support from the host organisation. In this case it would not be possible to utilise such a tool. Similarly, while FOSS software allows for flexibility and the ability to create one’s own code, a user will be dependent on the software being correctly maintained. This lack of warranty is an inherent issue with FOSS. With repositories like GitHub (and similar), it is possible to access, fork and publish or maintain one’s own repositories for tools at a personal or institutional level, providing licensing conditions allow.

The maintenance overhead should not be underestimated, especially when considering issues with business continuity. However, this is not a new problem and, if the value is seen, institutions can add resource to deliver long lasting FOSS solutions. Parallels can be drawn to the development of the Linux kernel. Here private companies contribute extensively to the FOSS development because there is an understanding of the value of that project to their business interest (20).

While FOSS approaches offer great benefits, the use of proprietary or closed source software is preferable when that software offers utility not possible by other routes. Complex analysis using statistical software, complex peak fitting or databases requiring subscriptions are still a reality of the profession. When these tools are needed the approach outlined above still works providing the data can be exported from such a program into a plain text format. If this is not possible and FAIR principles cannot be upheld, the use of such a tool should be re-evaluated to determine if its use can facilitate long term and sustainable analysis.

Emacs is a powerful and versatile tool for modern science. It facilitates the production, handling and analysis of data in a FAIR fashion while allowing modern scientists to be as agile as possible. By using tools under one FOSS umbrella huge productivity gains can be realised along with improvements in ergonomics and associated cost benefits with the removal of proprietary software tools. The learning curve should be viewed in the context of a lifelong scientific career. With institutions understanding the value of data beyond a single scientist, applying (or supporting individuals who wish to apply) this type of workflow more widely would have a profound and long last effect beyond the career of just one scientist.

Johnson Matthey’s surface spectroscopy team focuses on providing essential information on the surface chemistry and composition of different materials for Johnson Matthey businesses and their customers. The team develops in situ, ex situ multi-technique surface analysis methods to deliver a more in-depth surface characterisation (6). Using laboratory-based X-ray photoelectron spectroscopy (XPS) as the main surface analysis technique, the team works on the applications of several complementary spectroscopic techniques such as ion scattering spectroscopy (ISS), reflection electron energy loss spectroscopy (REELS), ultraviolet photoelectron spectroscopy (UPS) and Raman.

The surface spectroscopy team is also involved in developing synchrotron-based near-ambient pressure (NAP)-XPS applications to study materials under reaction conditions. The team has been supporting fundamental surface science investigations and has sponsored several PhD projects that involved NAP-XPS characterisation of catalysts under reaction conditions. Two recent PhD projects with the University of Reading involved synchrotron-based NAP-XPS measurements to study supported platinum group metal (pgm) catalysts under methane oxidation reaction conditions in situ.

The first PhD project focused on investigating the chemical and compositional changes in alumina supported palladium catalysts with different particle sizes (4 nm to 10 nm) under reaction conditions similar to those used in the partial oxidation of methane to synthesis gas (syngas) (7). Surface adsorbates, palladium oxidation states and partial pressures of reactants and products were simultaneously tracked using mass spectrometry and NAP-XPS. NAP‐XPS data showed how the oxidation state of the palladium changes with increasing temperature (from Pd[0] to PdO and back to Pd[0]). NAP-XPS data analysis was further enhanced using mass spectrometry which showed an increase in carbon monoxide production over the Pd[II] oxide phase. In this study, a particle size effect was revealed for the catalysts demonstrating that methane conversion starts at lower temperatures with larger sized particles (Figure 1) (8).

Fig. 1

For palladium catalysts on different supports such as alumina, silica and a mixture of alumina and silica, NAP-XPS showed that on all the supports studied PdO is the dominant oxidation state and is the active site for complete methane oxidation which occurs at 500–600 K. As the oxygen is consumed and the temperature increases to >650 K, PdO is found to reduce to PdO_x, where 0 ≤ x < 1. Mass spectrometry showed a decrease in the partial pressures of complete methane oxidation products (carbon dioxide and water). Syngas formation (hydrogen and carbon monoxide), the product of partial methane oxidation, is dominant, suggesting reduced palladium is the active state for partial methane oxidation. The reactivity of alumina supported palladium materials is found to increase in the order: SiO₂ < SiO₂-Al₂O₃ < Al₂O₃ (Figure 2) (8).

Fig. 2

Another collaborative PhD project (Johnson Matthey; Diamond Light Source, UK; and the University of Reading) involved studying the effect of pgm composition and reaction conditions (dry and wet) on the catalytic behaviour of a range of alumina supported monometallic palladium and bimetallic palladium-platinum nanocatalysts under methane oxidation conditions. NAP-XPS and in situ mass spectrometry were combined to correlate the product formation and the chemical state of the catalyst throughout the temperature ramps under methane and oxygen gas mixture at elevated temperatures under dry and wet conditions (Figure 3). NAP-XPS was used to study the chemical states of monometallic palladium and bimetallic palladium-platinum nanocatalysts, demonstrating that there is a clear link between platinum presence, palladium oxidation and catalyst activity under stoichiometric reaction conditions. Under oxygen-rich conditions this behaviour is found to be less clear, as all of the palladium tends to be oxidised, but there are still benefits to the addition of platinum in place of palladium for complete oxidation of methane (9).

Fig. 3

In this research, desized, 3/1 twill weave, 98% cotton and 2% elastane denim and non-denim fabrics (specific weight 340–370 g m⁻²) were used. The shell material ethyl cellulose was donated from Acros, Belgium. Shea butter (Tabia, Aydın) were employed as core materials. Tween^® 20 was used as a surfactant. The surface active agent, ethanol and ethyl acetate were supplied from Merck, Darmstadt, Germany. Nano polyurethane crosslinker (Tanatex, Switzerland) was used to bond the microcapsules to the fabric surface. All other auxiliary chemicals used in the study were of laboratory-reagent grade.

The fabric properties used in the study are shown in Table I.

In order to obtain the microcapsules, the capsules were prepared with the spray dryer method. In this process the interactions of water-insoluble polymers with water are utilised to form the microcapsules. Firstly, shea butter, which is solid at room temperature, was melted at 50°C. Ethyl cellulose and shea butter were dissolved homogenously in organic solvent in a specific ratio. Polymer-rich organic phase was added to polymer-free aqueous phase. Active ingredients were mixed with a Silverson high shear mixer. Afterwards, a spray dryer (SD-Basic LabPlant, Huddersfield, UK) was used to collect the microcapsules. The compositions were fed to the spray dryer at the following conditions for each batch size: feed flow rate of microencapsulating composition 10 ml min⁻¹; inlet air temperature 125°C and outlet air temperature 85°C. Microcapsules were collected from the product vessel using a soft brush in the fume hood and transferred to glass containers for storage. Chemical quantities and test conditions for spray dryer are given in Table II. Three different core:shell ratios were tested to obtain the optimal conditions for microencapsulation of shea butter. The most homogeneously distributed and high yield capsule production was optimised. For this purpose, the ratio of shea butter was varied to examine the encapsulation state of the active substances as shown in Table II.

The morphologic properties of the capsules were evaluated using SEM (Quanta^TM 250 FEG, FEI Co, USA). Samples were gold-coated (15 mA, 2 min) to assure electrical conductivity. The measurements were taken at 2 kV accelerating voltage. The images were taken at 5000× magnifcation.

To determine the size of the resulting optimum capsule, a Zetasizer Nano S (Malvern Panalytical, UK) particle-size distribution tester was used. Before measurement, an aqueous solution of capsules in a certain ratio was prepared and sonicated in an ultrasonic bath until a good mixture was formed. After that, the capsule dispersion was put in disposable cuvettes. Then, the light emitted by the laser Doppler was passed through the dispersion.

The total powder obtained after spray drying was weighed, and the process yield was calculated as a percentage of the amount of solids added during the preparation process according to Equation (i):

(i)

where R_% is the yield of the process, Q_i is the amount of solids initially added for the preparation of capsules and Q_f is the quantity of microcapsules obtained at the end of the process.

FTIR spectroscopy analysis was performed to determine encapsulation performance with the changes in the infrared (IR) spectrum for optimum capsule formulation. Measurements were taken at a wavelength range of 4000–400 cm⁻¹ using a PerkinElmer^® Frontier^TM FTIR device. The obtained spectra were smoothed to remove noise with the official software of the device.

Differential scanning calorimetry (DSC) was performed using a PerkinElmer^® PYRIS^TM Diamond differential scanning calorimeter for the purpose of distinguishing complex formation from simple physical mixing with the help of characteristic endothermic or exothermic peaks. The analyses were conducted in nitrogen medium between 0°C and 300°C. The scanning rate was stated as 5°C min⁻¹.

Denim and non-denim trousers to be used for capsule transfer were first subjected to the denim washing procedure. This procedure covers ageing processes that are made to give denim fabrics an aged appearance and can vary from very light tones to dark tones in line with customer demands. In this study, after rinse washing, stone washing and softening the trousers were turned over and the capsules were transferred.

The application of the selected optimum formulations to the fabrics was carried out with exhaustion and spraying methods. Capsule transfer was carried out according to Table III in the same ratio to compare the application processes. Nano-polyurethane was selected as binder and each experiment was repeated three times. The optimum capsule sample (6 g l⁻¹) and the binding agent (1.2 g l⁻¹) were dissolved in water and then transferred to the trousers with spraying and exhaustion methods.

The spraying and exhaustion operations were carried out in the Magic Box model Metaflow MET‐FLW drum machine shown in Figure 1. This system is used to coat denim jeans or any other textile garments with chemicals applied into the drum during tumbling. In the exhaustion method, the fabrics were treated with a bath containing a concentration of 5 g m⁻² microcapsules in the presence of binder at 40°C for 20 min in the drum machine. In the spraying method, 5 g m⁻² capsules and binder were sprayed at spraying speed of 100 g min⁻¹ with a spray system attached to the same machine. To achieve long lasting effect, fabrics were dried in a circulating air oven at 40°C for 20 min and just after drying, the fabric was mounted on pin frames and exposed for 5 min to 120°C in a laboratory stenter.

Fig. 1

After microcapsule application, the fabrics were washed according to ISO 3758:2012 (33) standard to determine the resistance to domestic repetitive washing and durability of capsules. In addition, rubbing tests were carried out according to ISO 105-X12:2016 (34) standard, because of the importance of the rubbing test for denim and non-denim trousers.

SEM images were taken to determine the existence of capsules on the textile surface from washed, unwashed and rubbed samples. Samples were gold-coated (15 mA, 2 min) to assure electrical conductivity. The measurements were taken at 2 V accelerating voltage. The images were taken at 5000× magnifcation.

DSC was performed using a PerkinElmer^® Diamond differential scanning calorimeter for the purpose of distinguishing the capsules on the denim and non-denim fabric with the help of characteristic endothermic or exothermic peaks. The analyses were conducted in nitrogen medium between 0°C and 300°C. The scanning rate was stated as 5°C min⁻¹.

Gas chromatography (GC) is a common type of chromatography used in analytical chemistry for separating and analysing compounds that can be vaporised without decomposition. The fabrics were extracted to analyse the contents of the capsules containing shea butter transferred to the fabrics. For GC analysis, an Agilent 7820a model GC‐MS + Headspace Sampler device (Agilent, USA) was used. GC device oven temperature was raised to 40°C and left for 3 min. Later, it was brought to 180°C by increasing at a rate of 7°C min⁻¹. After that, it was brought to 240°C by increasing at a rate of 30°C min⁻¹. It was left for 5 min at 240°C. An injector with a volume of 3 μl was used for sampling. Helium gas was used in the analysis. It was run in the ‘split’ mode at a flow rate of 1:200 at 1.5 ml min⁻¹. The injector temperature was set to 250°C and the column pressure to 37.1 kPa.

Air permeability analysis was performed on both denim and non-denim products according to TS 391 EN ISO 9237:1995 (ASTM D737-18) (35) standard in order to examine the effect of microcapsule application on the comfort properties of the products.

Tear strength of the fabrics were measured according to TS EN ISO 13937–2:2000 (36), using an Instron^® 4411 tensile strength tester. Elasticity and elastic recovery analyses were made according to ASTM D3107-072019 (37) standard.

An experimental framework in schematic form is provided in Figure 2.

Fig. 2

Spray dried microcapsules are usually characterised by spherical shape and narrow particle size distribution. Typical photomicrographs were obtained by SEM of the microcapsules and show that the spray-dried product is composed mainly of spherical shaped particles (Figure 3).

Fig. 3

According to SEM analysis, microcapsules filled with active substance (shea butter) were obtained. However, when the micrographs of S1 and S2 coded microcapsule were examined, it was observed that not all particles appeared morphologically spherical. Some of the microcapsules’ centres were collapsed and agglomerated due to sudden solvent evaporation when the polymer solution was introduced into the hot air chamber and also capsule distribution was not homogeneous (38). The biggest cause of shea butter-induced collapse in capsules is thought to be failure of the active ingredient to be encapsulated, resulting in its accumulation on the shell material. When SEM images of S2 coded microcapsules were examined it was seen that the size distribution and capsule shapes were not homogeneous. Therefore, according to SEM images of the microcapsules, the optimum shea butter content to get homogenous spherical microspheres was seen in S3 coded microcapsules. So, other characterisation and capsule transfer studies were carried out with the S3 coded capsule.

The mean particle size of microparticles was determined by laser diffraction method for microcapsules. Particle size distribution graphs of the microcapsules are indicated in Figures 4–6.

Fig. 4

Fig. 5

Fig. 6

The particle size of the three formulated capsules (S1, S2 and S3) loaded with shea butter ranged between 369 μm and 420 μm. The particle size results of the microcapsules are presented in Table IV.

When the particle size analysis of the capsules produced at different ratios was evaluated, the S1 coded capsules had a particle size of 400 nm and a high homogeneity. The particle size was 397 nm for S2 coded capsules. According to the data obtained as a result of the analysis, it was determined that 97.9% of the capsules were around 390 nm for S3 coded capsules. It was determined that the particle size analysis graph area showed a uniform distribution. When particle analysis results are evaluated, it was determined that shea butter based capsules with different ratios were homogeneously distributed and had a close value to each other.

The yield of microcapsules produced by a laboratory-scale spray dryer may not be high due to loss of lightweight particles by vacuum suction and adherence to the inside wall of the spray dryer apparatus. The mass yields ranged between 50.9% and 79.4% (w/w) as shown in Table V. A reduction in the amount of active substance in the formulation affected the production efficiency positively. In connection with SEM and particle size analysis, it has been determined that the failure of active substance to be encapsulated caused agglomeration in S1 (3:1 shea:ethyl cellulose) and S2 (2:1 shea:ethyl cellulose) coded capsule formulations.

The yield values of the capsule experiments with shea butter and three different molar ratios were calculated and it was concluded that the S3 coded capsules had the highest yield.

After SEM, particle size and mass yield of microcapsules analyses, S3 coded capsules were selected as the optimum ratio. For this reason, FTIR and DSC analyses were carried out over the determined S3 optimum capsules.

The FTIR spectra of shea butter capsules and the materials forming them are given in Figure 7.

Fig. 7

The characteristic peaks of shea butter were obtained in the FTIR spectra. In the hydrogen stretching region, the following peaks were seen: 2920 cm⁻¹, 2852 cm⁻¹, 2917 cm⁻¹, 2851 cm⁻¹. These bonds signal the presence of symmetric and asymmetric stretching vibration of the aliphatic CH₂ group. In the second spectral region of double bond stretching, frequency of 1741 cm⁻¹ occurred, which indicates that the ester carbonyl functional group of the triglycerides is present. The third region of deformation and bending in the functional group showed bonds at 1460 cm⁻¹ as well as 1370 cm⁻¹ and 1475 cm⁻¹ for shea butter. The peaks at 1460 cm⁻¹ indicate bending vibrations of the CH₂ and CH₃ aliphatic groups while 1370 cm⁻¹ and 1475 cm⁻¹ peaks showed bending vibration of the CH₂. In the fingerprint region, the bonds at 1243 cm⁻¹, 1165 cm⁻¹ and 1250 cm⁻¹, 1170 cm⁻¹ indicate the presence of shea butter. These bonds signal the stretching vibration of the C–O ester groups (39, 40).

When the IR spectrum of ethyl cellulose was examined, the stretching vibrations of characteristic –C–O–C– band and –C–H band were observed at 1054 cm⁻¹ and at 2870 cm⁻¹ and 2972 cm⁻¹, respectively. The C–C stretching vibration was located at 1640 cm⁻¹. When the spectra of the capsules were examined, both ethyl cellulose and shea butter peaks were identifed. The strong peak of ethyl cellulose at 1053 cm⁻¹ due to the –C–O–C– band was observed in the capsules. The C–H bands obtained at 2973 cm⁻¹ and 2870 cm⁻¹ were found to be deeper than ethyl cellulose peaks and close to the peak intensity of shea butter. This may indicate successful encapsulation.

After characterisation using SEM, particle size and mass yield of microcapsules, the S3 coded capsules were determined as the optimum formulation. The capsules were transferred to denim and non-denim fabrics by exhaustion and spraying methods and compared.

SEM images of denim and non-denim fabrics are indicated in Figure 8. These images show that capsule application succeeded for both exhaustion and spraying methods. It was observed that capsules were covered with the binder and fixed onto the textile surface for denim and non-denim fabrics. Also, the effect of repeated washings on capsules was evaluated. Capsules on the textile surface and embedded in the binder were observed even after five washes and rubbing test for both methods and fabrics.

Fig. 8

As a result of the SEM analysis, it was observed that the transfers made by the spraying method have similar results to the transfers made by the exhaustion method. It was concluded that capsules can also be transferred by the spraying method which can be used as an alternative to the conventional exhaustion method. Therefore, in order to determine the efficiency of the spraying method, analyses were carried out on the fabrics with capsules transferred by the spraying method.

The DSC diagrams of ethyl cellulose are given in Figure 9. When the DSC spectrum of shea butter is examined, based on data from Sigma Aldrich, the glass transition temperature (T_g) of ethyl cellulose is about 155°C. The melting temperature of shea butter is about 35–37°C in accordance with the literature (41). The endothermic movement observed at 169.64°C in the DSC analysis of ethyl cellulose is thought to be related to the glass transition temperature. When the microcapsules obtained with shea butter were examined, peaks corresponding to the melting point of the active substance are seen. It was concluded that these small peaks were due to the non-encapsulated active substance. When the DSC spectrum of microcapsules developed with shea butter and ethyl cellulose were examined, low intensity exothermic peaks were observed at 37°C. The reason for the low intensity of these peaks due to shea butter was attributed to being confined by ethyl cellulose.

Fig. 9

DSC diagrams of shea butter capsule transferred denim and non-denim fabrics are shown in Figure 10. When the DSC graphs of shea butter transferred fabrics were examined, similar graphs were seen in both denim and non-denim fabrics. This is thought to be due to the use of the same material (containing cellulose) as the raw material. As a result of SEM and DSC analysis, no difference could be detected between denim and non-denim fabrics (42, 43).

Fig. 10

The GC analysis results of fabrics transferred to microcapsules containing shea butter and the same samples after five washes are shown in Figure 11. As a result of GC analysis, shea butter peaks can be clearly seen in the GC diagrams. It was found that some of these peaks decreased after five washes, but significant peaks remained for shea butter. Oils transferred to the fabric in capsule form were protected.

Fig. 11

When the physical analysis results in Table VI are examined, it can be concluded that microcapsule application on both denim and non-denim products did not significantly affect the tear strength, elasticity and elastic recovery of the fabrics. The breaks in the warp direction were an expected result in denim products. If there is a break in the warp direction in denim fabrics, it means that the strength of the weft direction is above the standards and this situation is within acceptable limits for fabrics. The test results were evaluated by IBM^® SPSS^® software to examine the difference between the denim and non-denim fabrics. Results showed that the effect of fabric type is statistically significant for all microcapsule procedures and p value is 0.000 (p < 0.05).

In order to examine the effect of microcapsule application on the comfort properties of the products, air permeability analysis was performed on both denim and non-denim fabrics according to the TS 391 EN ISO 9237:1995 (ASTM D737‐18) (35) standard and the results are stated in Table VII. When the air permeability results are examined, it can be observed that microcapsule application on both denim and non-denim products did not significantly affect air permeability.

Punica granatum L. is a tree belonging to the Punicaceae family grown in Iran, Afghanistan, Turkey, the USA and Far East countries. The world pomegranate production is estimated as 1.5 million tonnes annually (1). Pomegranate juice is popular and is claimed to include antibacterial, anticancer, antioxidant, antiallergic and anti-inflammatory compounds (2, 3). Recent reports also showed its potential use in the treatments of cardiovascular diseases and diabetes (4).

The pomegranate peel amounts to more than half of the weight of the pomegranate. It is a fruit processing-waste but it could be used as a source of antioxidants, phenols, flavonoids and organic acids. Extracts of pomegranate peels including rind, husk, pericarp and membranes are rich in polyphenols (ellagitannin and punicalagins), gallic acid, flavones, flavanones and anthocyanidins (5–7). These phenolic compounds and organic acids can be extracted by using different solvents. The efficiency of extraction is mostly dependant on the type of solvent, time and temperature. Therefore, it is important to determine the best solvent and extraction method to obtain the best bioactive compound rich extracts. Many extraction methods are reported for extraction of phenolic compounds from pomegranate by using ethanol, methanol, acetone, ethyl acetate and water (8, 9).

The presence of punicalagins, punicalin, ellagic acid and gallic acid in PPEs could determine their antimicrobial activities on microorganisms (10, 11). PPEs prepared by using ethanol, methanol or their mixtures with water were shown to be effective on Staphylococcus aureus, Enterobacter aerogenes, Klebsiella pneumoniae and Salmonella typhi strains (10). Abdollahzadeh et al. (12) reported that the methanolic extract of pomegranate peel exhibited antimicrobial activities against oral pathogens including S. aureus and Staphylococcus epidermidis strains. Moreover, PPEs containing polyphenols, tannins and other secondary metabolites showed effective antibacterial activity against shiga toxin producing E. coli (STEC) (13). Extracts from other parts of pomegranate (such as rinds, membranes and seeds) also had antimicrobial effects on S. aureus and Bacillus megaterium (14). To the best of our knowledge there is no report for evaluation of antimicrobial activities of different PPEs for Turkish Hicaz variety. Therefore, in the present study the most efficient extraction methods were investigated to obtain potential natural antimicrobial compounds from Turkish Hicaz pomegranate peel that can be used as a source of safe preservatives in the food industry. For this, different solvents (ethanol, methanol, either alone or in combination with acid, acetone and water) were used for extraction of phenolics and organic acids from pomegranate fruit peels obtained as a waste from fruit juice processing industry. Extracts were then screened for their antimicrobial activities against some important microorganisms.

2.1 Preparation of Pomegranate Waste

2.2 Chemicals

2.3 Preparation of Pomegranate Peel Extracts

2.4 Qualitative Analysis of Phenolics and Sugars

2.5 Quantitative Analysis of Phenolics, Sugars and Organic Acids

2.6 Antimicrobial Analysis

2.7 pH Measurements

2.8 Statistical Analysis

3.1 Phenolics

3.2 Sugars

3.3 Organic Acids

3.4 pH Values

3.5 Antimicrobial Activities of Pomegranate Peel Extracts

In the present work, PPEs were prepared from Turkish Hicaz pomegranate variety by using ethanol, methanol or their combinations with acid, acetone and water. All extracts were found to have antimicrobial effects on different bacteria and a fungus. Amongst the evaluated extracts, MA and EA exhibited the largest inhibition zones for all tested microorganisms. It was shown that high amounts of organic acids (for MA and EA) and phenolics (for EA) could be responsible for the antibacterial activities. Further studies are required to isolate other potential bioactive compounds of the PPEs and to identify their molecular mechanisms of action. In addition, potential PPEs from different varieties of pomegranate should also be investigated to obtain the most valuable bioactive compounds that could be used as safe food preservatives in the food industry.

The global effort to produce solar fuels by means of molecular photocatalysis continues to intensify following the establishment of basic design principles in the 1970s and 1980s (1–5). Many of the challenges encountered at the time remain relevant today, namely molecular systems for photocatalytic water oxidation or reduction often rely on a sacrificial electron source to drive the thermodynamically demanding multielectron reaction, most of which are not environmentally benign or renewable (6). However, a direct photocatalytic system would avoid the fabrication and systems costs required with photovoltaics coupled to electrolysis. Tremendous effort has been expended in an attempt to understand photocatalytic processes in solution either as a bimolecular process for homogeneous catalysis or on a semiconductor surface in a heterogeneous system (7–11). Over the years a vast catalogue of new molecular catalysts and sensitisers has been generated, a prototypic example being the well-known ruthenium water oxidation catalyst reported by Meyer et al., the “blue dimer” (12). Recent efforts have produced catalysts based on earth abundant metals such as iron and cobalt which typically underperform compared to prior systems, leaving the economic and environmental considerations for moving away from precious metal systems (platinum, palladium, rhodium or ruthenium) under some debate (13–15).

Sensitisers based on the thoroughly understood ruthenium(II) trisbipyridyl complex remain popular as a starting point for new avenues of research (16–19). More recent efforts have focused on covalently linking the catalyst and sensitiser to optimise charge separation (20). The fundamental principle of photocatalyst design where donor and acceptor are joined by a suitable covalent bridge is elegant and inspired by the photosynthetic reaction centre of green plants, but challenging to optimise in a working photochemical reactor, even where the molecular systems are relatively simple (21, 22). To date, practical implementations of molecular photocatalytic systems are scarce, despite the variety of systems proposed. Surface and solvent parameters introduce a large set of variables for optimising a photocatalytic reactor. At this point there are now an abundance of molecules and materials but developing a mechanistic understanding of such systems to aid design is still an ongoing process (23).

Over the past decade, research into dye-sensitised solar cells (DSSCs) has converged with heterogeneous photocatalysis (1, 24–27). The potential advantages include high atom efficiency for the catalyst, low cost assembly through solution-based processing, and devices that operate under ambient conditions. Dye-sensitised photocathodes have been constructed in photoelectrochemical devices to reduce protons to hydrogen, usually under a small applied bias for a half-cell in a photocatalytic system (28–31). These can be coupled to photoanodes which provide the electrons for proton reduction as a byproduct of water oxidation in a tandem device (31). The tandem system, where both anode and cathode are decorated with photocatalysts, removes the need for sacrificial reagents and enables a sustainable system (32). These dye-sensitised photoelectrochemical cells overcome many of the disadvantages of molecular systems in solution, while taking advantage of the catalogue of sensitisers and catalysts already available. Light absorption, charge transport and catalysis are separated between three tuneable components (33–35). Attention then turns to the interfaces between these components, for example, how photocatalysts attach and behave on the semiconductor surface, which requires considerable optimisation.

In this contribution we focus on optimising the proton reduction half reaction, generating hydrogen by means of new integrated photocatalysts adsorbed onto the surface of a transparent p-type semiconductor, nickel(II) oxide. A proof-of-concept for this approach was reported by us recently for two supramolecular photocatalysts based on a bipyridyl ruthenium photosensitiser coupled to either a platinum or palladium catalytic centre via a terpyridine or triazole bridging ligand (36). In this follow up paper, we explore a series of new photocatalysts with structural modifications made to the bridge and the catalytic centre, and evaluate the impact these have on the photoelectrocatalytic reduction of protons (Figure 1). Consideration is given as to how the photocatalysts bind to the electrode surface and interact with the surrounding environment. Device testing in a photoelectrochemical cell is used to determine photocurrent and hydrogen produced as a result, and stability is evaluated over prolonged periods of illumination. The photophysical and electrochemical properties of the photocatalysts are examined to develop a mechanistic understanding of the system.

Fig. 1.

2.1 Optical Properties

The preparation and characterisation of mesoporous nickel oxide cathodes has been described previously (37). Adsorption of the photocatalysts onto the mesoporous nickel oxide films on NSG TEC^TM 15 conductive glass from acetonitrile solutions was recorded by ultraviolet-visible (UV-vis) spectroscopy. Normally in titania-based systems the ester group is hydrolysed prior to adsorption or the titania is treated with base to promote binding (38), but for nickel oxide we have found that these ester-functionalised systems bind as well as the carboxylic acid derivatives. To calculate the dye loading, we assumed that the molar absorption of the dyes did not change significantly on the nickel oxide surface compared to the dye in solution. In dry acetonitrile, all five dyes produce steady-state absorption spectra with characteristic metal to ligand charge transfer (MLCT) bands from ruthenium to the diethyl ester bipyridyl ligands (39). When immobilised on nickel oxide films, the overall trend is towards a broader, red-shifted spectrum. While we do not have a model regarding what causes this spectral shift, it indicates that the dye interacts with the nickel oxide surface through the ester anchoring groups. This spectral broadening is typical for dyes adsorbed on metal oxide surfaces, and is usually observed when carboxylic acid anchoring groups are used and it is possibly caused by deprotonation of the acid on binding (40, 41). In this case, where ester anchoring groups have been used it could be due to hydrolysis or, possibly, the result of overlap from several different orientations of the catalyst on the nickel oxide surface, including some aggregates (42). The steady state absorption spectra are shown in Figure 2 and the data are summarised in Table I.

Fig. 2.

UV-vis absorption spectra of PC1–PC5: (a) in acetonitrile solution; (b) adsorbed on nickel oxide. Solution spectra contained micromolar concentration of dye

Table I

Steady-State UV-Visible Absorption Data for the Photocatalysts

Catalyst	(solution), nm	(film), nm	ɛ, M–1 cm–1 at (MeCN)	Dye loading, nmol cm–2
PC1	480	510	28,800	5.9
PC2	498	520	27,600	7.0
PC3	480	520	42,500	2.9
PC4	527	530	42,000	6.2
PC5	467	470	30,500	6.2

2.2 Transient Absorption Spectroscopy

2.3 Photoelectrocatalysis

2.4 X-Ray Photoelectron Spectroscopy

As with our previous studies with integrated photocatalysts based on a similar architecture, ester groups on the bipyridyl ligands serve as an anchoring site to nickel oxide (36). Our previous findings clearly suggested that there was sufficient interaction between the ester groups and the nickel oxide surface to provide a system resistant to dye desorption during photocatalysis (under suitable conditions). XPS results largely corroborate these claims. The XPS results show that the buffer salts can assemble on the electrode surface, and it is possible that there is competition between organic salts and the photocatalyst, leading to some desorption. We continue to favour ester substituted photocatalysts due to the simplified synthesis and purification of the dye. However, the diethoxy ester bipyridyl ligand serves two functions, both as the anchoring group to the nickel oxide surface and as an electron withdrawing ligand which stabilises the MLCT excited states. Locating the electron density close to the nickel oxide surface could increase the rate of charge-recombination, so the best photocatalysts should promote charge-transfer to the catalyst centre via the bridging ligand (20).

All the photocatalysts in this study produced photocurrent and hydrogen under each varied condition. Some photocatalysts perform better at pH 3, whereas others performed better at pH 7. The performance with pH 5 acetate buffer was the most consistent between different catalysts, but was generally lower than the other two buffers. Changing to MES buffer improved the performance at pH 5. Different authors report contrasting results when changing the pH of the electrolyte for analogous. The type of buffer affects the performance in two ways. Lower pH should increase the rate of hydrogen formation from the catalyst (50), however, the effect of pH on the valence band edge of the nickel oxide is a shift to more positive potentials as the H⁺ concentration increases (51). Further transient absorption spectroscopy studies are necessary to probe the effect of pH on the dynamics. It is expected that charge transfer from the nickel oxide to the photocatalyst would be slower at higher pH due to the lower valence band edge and, therefore, smaller driving force (52). Charge-recombination should occur in the Marcus inverted region and would be expected to be slower at higher pH, and increase the charge-collection efficiency of the device. However, in most kinetic studies, charge-recombination at the photosensitiser/nickel oxide interface appears to follow Marcus normal behaviour, accelerating with increasing driving force, probably due to recombination with more energetic intra-bandgap defect states (53, 54).

While this study shows that there are a variety of ways to improve the performance of dye-sensitised photoelectrochemical devices, including tuning the structure of the catalyst or the environment, the performance of dye-sensitised photocathodes based on nickel oxide still lags behind equivalent photoanode devices based on titania. Further improvements are necessary, for example, improving the porosity of the electrodes to facilitate mass transport. Additionally, bubble formation on the electrode surface leads to a drop in the photocurrent and good diffusion is necessary to prevent a build-up of OH^– in the pores. At the same time, high surface area must be maintained to adsorb enough photocatalyst to absorb all the incident light. A limitation of the ruthenium bipyridyl chromophores is the low absorption coefficient (ɛ = 27,600–42,500 M^–1 cm^–1 for PC1–PC5). Organic photosensitisers have two to three times larger absorption coefficients, enabling thinner or more porous films to be used (33, 55, 56). Future considerations for dye-sensitised photoelectrochemical assemblies should also consider the prospect of engineering the thermodynamic driving force for charge recombination towards the Marcus inverted region (similar to titania-based systems). Ongoing work within our laboratory looks to find an alternative semiconductor to nickel oxide with a more typical band structure to address this (32, 57).

The present study describes a series of novel sensitiser-catalyst dyads that were tested in a photoelectrocatalytic half-cell set-up to determine the optimum conditions for water splitting. At a range of pH levels the dyads remained intact with a stable photocurrent, demonstrating their robustness. However, when using a non-coordinating buffer we saw an increase in photocurrent. All our systems produced hydrogen under a small applied bias. Under optimal conditions, Faradaic efficiencies up to 90% with turnover numbers of up to 33 in an hour were achieved, which are comparative to our previously reported systems. Transient absorption measurements confirmed that charge separation on the surface of a p-type semiconductor, nickel oxide, is very efficient when compared to the same system in dilute solution. While rapid charge injection facilitates reduction of the catalyst, thus aiding photocatalysis, recombination of the oxidised sensitiser with nickel oxide remains equally efficient. The insight from transient absorption data in particular highlights a major challenge for integrated molecular photocatalysts on a semiconductor interface and further work will seek to address this.