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Applied Sciences
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New Trends in Electrode for Electrochemical Analysis
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New Trends in Electrode for Electrochemical Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Chemical and Molecular Sciences".

Deadline for manuscript submissions: 30 December 2026 | Viewed by 532

Special Issue Editors

Prof. Dr. Luis Humberto Mendoza-Huizar

E-Mail Website
Guest Editor
Área Académica de Química, Universidad Autónoma del Estado de Hidalgo, Carretera Pachuca-Tulancingo Km 4.5, C.P. 42184, Mineral de La Reforma, Hidalgo, Mexico
Interests: electrodeposition; electroanalysis; computational chemistry; nanomaterials; ultramicroelectrodes
Dr. Giaan Arturo Álvarez-Romero

E-Mail Website
Guest Editor
Área Académica de Química, Universidad Autónoma del Estado de Hidalgo, Carr. Pachuca-Tulancingo Km. 4.5, Mineral de la Reforma, Pachuca 42184, Hidalgo, Mexico
Interests: electroanalysis; nanomaterials; analytical methods; conducting polymers; sensors; biosensors

Special Issue Information

Dear Colleagues,

Electrochemical analysis has emerged as a vital technique in a wide range of scientific fields, from environmental monitoring to biomedical diagnostics. At the core of these analyses are electrodes, which are essential for ensuring the sensitivity, selectivity, and overall performance of electrochemical sensors and devices. This Special Issue, 'New Trends in Electrode for Electrochemical Analysis', focuses on the latest advancements in electrode materials and fabrication methods, which are driving innovation in electrochemical analysis. New electrode materials, such as nanostructured and composite electrodes, are significantly enhancing electrochemical performance, stability, and versatility, enabling breakthroughs in areas like sensing, catalysis, and energy storage. Key developments include the use of nanomaterials and advanced surface modification techniques to improve electrode functionality. Moreover, innovations in electroanalysis techniques, such as the machine learning-guided design of electroanalytical pulse waveforms and the development of ultramicroelectrodes and nanoelectrodes, are advancing the precision and sensitivity of electrochemical measurements. This issue delves into these emerging trends and their impact on the future of electrochemical analysis, exploring how novel electrode materials, fabrication techniques, and electroanalysis approaches are shaping the next generation of electrochemical devices.

Prof. Dr. Luis Humberto Mendoza-Huizar
Dr. Giaan Arturo Álvarez-Romero
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • electrochemical analysis
  • electrode materials
  • electrochemical sensors
  • nanostructured electrodes
  • composite electrodes
  • nanomaterials
  • electroanalysis techniques
  • machine learning-guided design
  • electroanalytical pulse waveforms
  • ultramicroelectrodes
  • nanoelectrodes

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Published Papers (1 paper)

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Research

22 pages, 6997 KB  
Article
Deep-Learning-Based Time-Series Forecasting of Hydrogen Production in a Membraneless Alkaline Water Electrolyzer: A Comparative Analysis of LSTM and GRU Models
by Davut Sevim, Muhammed Yusuf Pilatin, Serdar Ekinci and Erdal Akin
Appl. Sci. 2026, 16(8), 3938; https://doi.org/10.3390/app16083938 - 18 Apr 2026
Viewed by 174
Abstract
Hydrogen production is gaining increasing importance as a key component of the transition toward carbon-neutral energy systems. In this study, the prediction of hydrogen generation in membraneless alkaline water electrolyzers (MAWEs) is investigated using deep-learning-based time-series modeling. A single-input modeling framework is adopted, [...] Read more.
Hydrogen production is gaining increasing importance as a key component of the transition toward carbon-neutral energy systems. In this study, the prediction of hydrogen generation in membraneless alkaline water electrolyzers (MAWEs) is investigated using deep-learning-based time-series modeling. A single-input modeling framework is adopted, where only the system current is used as the input variable. Experimental current signals obtained from long-duration tests conducted at electrolyte concentrations between 5 and 35 g KOH (7200 s per experiment) are employed as the model inputs, while mass-based hydrogen production (in grams) is used as the output variable. Two recurrent neural network architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are implemented, and their predictive performance is comparatively evaluated using RMSE, MAE, and R2 metrics. In addition to deep learning models, classical approaches including Linear Regression, ARIMA, and Naïve Forecast are also considered for comparison. The results show that both models are capable of accurately reproducing the hydrogen-production dynamics across the entire concentration range. In particular, the prediction accuracy improves notably at medium and high electrolyte concentrations, where the coefficient of determination (R2) approaches 0.98. The residual distributions remain narrow and symmetric around zero, indicating the absence of systematic estimation bias. The results also show that classical models can achieve comparable performance under stable operating conditions, while deep learning models provide advantages in capturing nonlinear and dynamic behavior. While LSTM and GRU exhibit comparable accuracy, each architecture provides complementary advantages under different operating conditions. These findings indicate that deep-learning-based time-series modeling constitutes a lightweight and reliable framework for prediction and control applications in MAWE systems. Overall, this study demonstrates the applicability of data-driven models for the dynamic characterization of membraneless water electrolysis. Full article
(This article belongs to the Special Issue New Trends in Electrode for Electrochemical Analysis)
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