Numerical Simulations in Electrochemistry

Speiser, Bernd

doi:10.1007/978-1-4419-6996-5_34

Cited by 3 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accordingly, and as would be expected, data optimisation methods have been advocated for parameterisation of DC voltammetry for many years. 34,35 However, as noted in a recent opinion article, 36 applications of computationally supported data analysis to solve the so-called "inverse" problem to extract quantitative information from voltammograms have been remarkably rare. 17,[37][38][39] Furthermore, parameter optimisation methods used to date to quantify DC and other voltammetric data have almost exclusively provided only point estimates of a small number of parameters related to the faradaic process without uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Automated acquisition of experimental data [ 7 , 8 ] and computer simulations of electrochemical systems [ 9 , 10 ] play an important role in modern electrochemistry. Due to the wide applicability and high speed of voltammetric experiments [ 3 ], data analysis methods are required to aid electrochemists in extracting knowledge about electrochemical systems [ 11 - 14 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

2014

Self Cite

View full text Add to dashboard Cite

BackgroundSupport vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients.ResultsFor simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes.ConclusionsEstimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data.

show abstract

Exploiting Plug-And-Play Electrochemistry for Drug Discovery

Gao

Teng

2016

Future Med. Chem.

View full text Add to dashboard Cite

Electrochemistry has emerged as a powerful analytical technique for chemical analysis of living cells, biologically active molecules and metabolites. Electrochemical biosensor, microfluidics and mass spectrometry are the most frequently used methods for electrochemical detection and monitory, which comprise a collection of extremely useful measurement tools for various fields of biology and medicine. Most recently, electrochemistry has been shown to be coupled with nanotechnology and genetic engineering to generate new enabling technologies, providing rapid, selective, and sensitive detection and diagnosis platforms. The primary focus of this review is to highlight the utility of electrochemical strategies and their conjunction with other approaches for drug metabolism and discovery. Current challenges and possible future developments and applications of electrochemistry in drug studies are also discussed.

show abstract

Numerical Simulations in Electrochemistry

Cited by 3 publications

References 19 publications

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

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

Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

Exploiting Plug-And-Play Electrochemistry for Drug Discovery

Contact Info

Product

Resources

About