Publications associées à Review on Uric Acid Recognition by MOFs with a Future in Machine Learning

Solution-based Cu+ transient species mediate the reconstruction of copper electrocatalysts for CO2 reduction

Raffaella Buonsanti, Anna Loiudice, Jan Vávra, Petru Pasquale Albertini, Gaétan Philippe Louis Ramona

Understanding metal surface reconstruction is of the utmost importance in electrocatalysis, as this phenomenon directly affects the nature of available active sites. However, its dynamic nature renders surface reconstruction notoriously difficult to study. ...

Nature Portfolio2024

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

Sophia Haussener, Etienne Boutin

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 ...

2024

NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection

Lijing Xin, Yan Lin, Wei Ye, Yubo Zhao

Metabolic changes precede malignant histology. However, it remains unclear whether detectable characteristic metabolome exists in esophageal squamous cell carcinoma (ESCC) tissues and biofluids for early diagnosis. Here, we conduct NMR- and MS-based metabo ...

Nature Portfolio2024

Microstructure - Electrochemical Surface Reactivity Relationship of Electrochemically Grown Manganese Oxides Films

Roberto Cestaro

This Thesis provides a comprehensive and correlative investigation of the microstructure, chemical state, and electrochemical reactivity of manganese oxide (MnOx) films, aiming to gain a deeper understanding of the deposition and dissolution mechanism of M ...

EPFL2024

Tailoring nanocatalysts and reaction interfaces for water- and CO2-electrolysis

Thi Ha My Pham

Renewable energy sources offer a promising solution for mitigating sustainability and CO2 emissions-related issues due to their vast energy generation capacity. They enable hydrogen production via water electrolysis, as well as carbon capture and utilizati ...

EPFL2024

Dynamic Behavior of Spatially Confined Sn Clusters and Its Application in Highly Efficient Sodium Storage with High Initial Coulombic Efficiency

Xu Xu, Kumar Varoon Agrawal, Lei Zhang, Kangning Zhao

Advanced battery electrodes require a cautious design of microscale particles with built-in nanoscale features to exploit the advantages of both micro- and nano-particles relative to their performance attributes. Herein, the dynamic behavior of nanosized S ...

Wiley-V C H Verlag Gmbh2024

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

Berend Smit, Sauradeep Majumdar, Xiaoqi Zhang, Hyunsoo Park

The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the e ...

Royal Soc Chemistry2024

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

Sophia Haussener, Evan Fair Johnson

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 ...

Amer Chemical Soc2024

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

Mohammad Khaja Nazeeruddin

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

Elsevier Science Sa2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

HaloTag-Based Tools for Live-Cell Imaging and Elucidation of Organellar Redox Biology

Sarah Emmert

Redox homeostasis is a key factor in maintaining cellular function and health. The main determinant of the intracellular redox potential is ubiquitous glutathione (GSH) together with its oxidized dimer (GSSG). Importantly, both redox equilibrium and GSH ho ...

EPFL2024

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

Arnout Jan J Devos

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 ...

EPFL2024

Pathways to enhance electrochemical CO2 reduction identified through direct pore-level modeling

Sophia Haussener, Shuo Liu, Etienne Boutin, Evan Fair Johnson

Electrochemical conversion of CO2 to fuels and valuable products is one pathway to reduce CO2 emissions. Electrolyzers using gas diffusion electrodes (GDEs) show much higher current densities than aqueous phase electrolyzers, yet models for multi-physical ...

2023

Insights into Electrocatalyst Transformations Studied in Real Time with Electrochemical Liquid-Phase Transmission Electron Microscopy

Vasiliki Tileli, Tzu-Hsien Shen, Robin Pierre Alain Girod

The value of operando and in situ characterization methodologies for understanding electrochemical systems under operation can be inferred from the upsurge of studies that have reported mechanistic insights into electrocatalytic processes based on such mea ...

Amer Chemical Soc2023

Medical and Personal Characteristics Can Predict the Risk of Lung Metastasis

Michele De Palma, Nahal Mansouri

Aims: Understanding the correlations between underlying medical and personal characteristics of a patient with cancer and the risk of lung metastasis may improve clinical management and outcomes. We used machine learning methodologies to predict the risk o ...

2023

Electron imaging of gas diffusion electrodes for proton exchange membrane fuel cells

Michele Bozzetti

The current energy crisis demands the development of new sustainable energy sources and devices with low environmental impact, including in the field of electrochemical cells. Mass transport limitations can reduce the overall performance of the device by l ...

EPFL2023

Voltage-driven Reactivity of Liquid Ga and CuGa Nanoparticles for Electrocatalytic CO2 Reduction and Synthesis

Valery Okatenko

The rising atmospheric level of carbon dioxide, CO2, contributes to climate change and poses urgent need to find scalable solutions both to decrease its emissions and to be able to recycle it, if the goals of Paris Agreement to limit the temperature rise a ...

EPFL2023

In-vivo investigation of electrochemical behavior of Ti and CoCrMo alloy in human synovial fluids

Yueyue Bao

With the development of modern medical techniques, the application of orthopedic implants has been continually increasing in recent years. Passive materials, such as Ti and its alloys as well as CoCrMo alloys, are widely used in artificial joints due to th ...

EPFL2023

Development of new volcano-shaped microelectrodes for single cell electrochemistry, electrophysiology and impedance studies

Nicolas Grégoire Maïno

Micro- nano-electrodes have demonstrated superior performances in measuring attenuated intracellular action potentials from electrogenic cell cultures compared to traditional multi-electrode arrays. Yet, the understanding of the critical electrode features ...

EPFL2023

Catalyst Aggregate Size Effect on the Mass Transport Properties of Non-Noble Metal Catalyst Layers for PEMFC Cathodes

Yen-Chun Chen, Vasiliki Tileli, Robin Pierre Alain Girod, Michele Bozzetti, Secil Unsal

Non-noble metal catalysts (NNMCs) are regarded as a promising alternative to the costly Pt-based materials required to catalyze the oxygen reduction reaction (ORR) in proton exchange membrane fuel cell (PEMFC) cathodes. However, the large diversity of NNMC ...

ELECTROCHEMICAL SOC INC2023