Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

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In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Living photovoltaics are microbial electrochemical devices that use whole cell–electrode interactions to convert solar energy to electricity. The bottleneck in these technologies is the limited electron transfer between the microbe and the electrode surfac ...

In the field of electrochemical CO2 reduction, both continuum models and molecular dynamics (MD) models have been used to understand the electric double layer (EDL). MD often focuses on the region within a few nm of the electrode, while continuum models ca ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Silver is one of the most studied electrode materials for the electrochemical reduction of carbon dioxide into carbon monoxide, a product with many industrial applications. There is a growing number of reports in which silver is implemented in gas diffusio ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Anion exchange membrane water electrolysis (AEMWE) offers a green hydrogen production method that eliminates the need for platinum group metals (PGM) as electrocatalysts. This study employs a COMSOL (R) 6.0 model to simulate a 1x1 cm(2) Ni fibre - Raney (R ...

Permissible limits of Pb 2+ in drinking water are being reduced from 10 mu gL -1 to 5 mu gL -1 , which calls for rapid, and highly reliable detection techniques. Electrochemical sensors have garnered attention in detection of heavy metal ions in environmen ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

The World Health Organization highlights the urgent need to address the global threat posed by antibiotic-resistant bacteria. Efficient and rapid detection of bacterial response to antibiotics and their virulence state is crucial for the effective treatmen ...

A common method for creating compliant electrodes for dielectric elastomer actuators (DEAs) and soft sensors is to incorporate electrically conductive carbon particles into a polymer matrix. However, using unidirectional aligned carbon fibers instead not o ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Dielectric elastomer actuators (DEAs) have raised interest due to their remarkable capabilities in various applications, such as soft robotics, haptic feedback systems, and biomedical devices. To harness the full potential of DEAs, the choice of the electr ...

In recent years, many efforts have been made to find alternative renewable energy sources that can ideally replace the use of fossil fuels in all aspects. One of the new emerging energy technologies is the bioelectrochemical system, of which two types are ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

To support the decarbonisation of the power sector and offset the volatility of a system with high levels of renewables, there is growing interest in residential Demand-Side Management (DSM) solutions. Traditional DSM strategies require consumers to active ...

Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly w ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

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Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Random matrix methods for high-dimensional machine learning models

Polydopamine-coated photoautotrophic bacteria for improving extracellular electron transfer in living photovoltaics

Contrasting Views of the Electric Double Layer in Electrochemical CO2 Reduction: Continuum Models vs Molecular Dynamics

Driving and suppressing the human language network using large language models

Rate-Determining Step for Electrochemical Reduction of Carbon Dioxide into Carbon Monoxide at Silver Electrodes

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Membrane electrode assembly simulation of anion exchange membrane water electrolysis

Electrochemical sensors modified with iron oxide nanoparticles/ nanocomposites for voltammetric detection of Pb (II) in water: A review

Topics in statistical physics of high-dimensional machine learning

Machine learning method for the classification of the state of living organisms' oscillations

Highly anisotropic carbon fiber electrodes for DEAs and their dynamic non-monotonic conductive properties

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Carbon based printed electrodes for DEAs: study of pad, inkjet, and stencil printing

Green hydrogen production using Shewanella oneidensis MR-1 bioanode and cuprous oxide-based photocathode

Partial discharge localization in power transformer tanks using machine learning methods

Beyond the average consumer: Mapping the potential of demand-side management among patterns of appliance usage

Efficient local linearity regularization to overcome catastrophic overfitting

Machine Learning for Modeling Stock Returns