Leveraging e-Science infrastructure for electrochemical research

Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry

Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry. Nowadays such approaches can be implemented routinely with widely available, user-friendly modern computing languages, algorithms and high speed computing to provide accurate and robust methods for quantitative comparison of experimental data with extensive simulated data sets derived from models proposed to describe complex electrochemical reactions. While the methodology is generic to all forms of dynamic electrochemistry, including the widely used direct current cyclic voltammetry, this review highlights advances achievable in the parameterisation of large amplitude alternating current voltammetry. One significant advantage this technique offers in terms of data analysis is that Fourier transformation provides access to the higher order harmonics that are almost devoid of background current. Perspectives on the technical advances needed to develop intelligent data analysis strategies and make them generally available to users of voltammetry are provided.

Probing biological redox chemistry with large amplitude Fourier transformed ac voltammetry

Biological electron-exchange reactions are fundamental to life on earth. Redox reactions underpin respiration, photosynthesis, molecular biosynthesis, cell signalling and protein folding. Chemical, biomedical and future energy technology developments are also inspired by these natural electron transfer processes. Further developments in techniques and data analysis are required to gain a deeper understanding of the redox biochemistry processes that power Nature. This review outlines the new insights gained from developing Fourier transformed ac voltammetry as a tool for protein film electrochemistry.

Limitations in Electrochemical Determination of Mass-Transport Parameters: Implications for Quantification of Electrode Kinetics Using Data Optimisation Methods

Voltammetric quantification of the electrode kinetics for the quasi-reversible reaction requires detailed experiment–theory comparisons. Ideally, predicted data derived from the theoretical model are fitted to the experimental data by adjusting the reversible potential (E0), heterogeneous electron transfer rate constant at E0 (k0), and charge transfer coefficient α, with mass-transport and other parameters exactly known. However, parameters relevant to mass transport that include electrode area (A), diffusion coefficient (D), and concentration (c), are usually subject to some uncertainty. Herein, we examine the consequences of having different combinations of errors present in A, D, and c in the estimation of E0, k0, and α on the basis of the a.c. (alternating current) voltammetric experiment–theory comparisons facilitated by the use of a computer-assisted parameter optimisation algorithm. In most cases, experimentally reasonable errors (<10 %) in the mass-transport parameters do not introduce significant errors in recovered E0, k0, and α values. However, a pernicious situation may emerge when a slight overestimation of A, D or c is included in the model and results in erroneous identification of a reversible redox process as a quasi-reversible one with a report of apparently quantifiable kinetic parameters k0 and α.

Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

Recently, a number of organisations have called for open access to scientific information and especially to the data obtained from publicly funded research, among which the Royal Society report and the European Commission press release are particularly notable. It has long been accepted that building research on the foundations laid by other scientists is both effective and efficient. Regrettably, some disciplines, chemistry being one, have been slow to recognise the value of sharing and have thus been reluctant to curate their data and information in preparation for exchanging it. The very significant increases in both the volume and the complexity of the datasets produced has encouraged the expansion of e-Research, and stimulated the development of methodologies for managing, organising, and analysing "big data". We review the evolution of cheminformatics, the amalgam of chemistry, computer science, and information technology, and assess the wider e-Science and e-Research perspective. Chemical information does matter, as do matters of communicating data and collaborating with data. For chemistry, unique identifiers, structure representations, and property descriptors are essential to the activities of sharing and exchange. Open science entails the sharing of more than mere facts: for example, the publication of negative outcomes can facilitate better understanding of which synthetic routes to choose, an aspiration of the Dial-a-Molecule Grand Challenge. The protagonists of open notebook science go even further and exchange their thoughts and plans. We consider the concepts of preservation, curation, provenance, discovery, and access in the context of the research lifecycle, and then focus on the role of metadata, particularly the ontologies on which the emerging chemical Semantic Web will depend. Among our conclusions, we present our choice of the "grand challenges" for the preservation and sharing of chemical information.