Results 161 to 170 of about 97,116 (199)

Wood‐Based Bioelectronics: Lignosulfonate‐Based Conductive Biocomposites for Paper Organic Electrochemical Transistors

open access: yesAdvanced Electronic Materials, EarlyView.
Biodegradable wood‐based bioelectronics are realized by integrating poly (2,3‐ethylenedioxythiopene:lignosulfonate (PEDOT:LigS) as a mixed ionicelectronic channel in organic electrochemical transistors fabricated on paper substrates. The biocomposite exhibits high conductivity, biocompatibility, and strong transistor performance, while devices built on
Katharina Matura   +8 more
wiley   +1 more source

Main‐Chain Ion‐Pair Polybenzimidazole Membranes Enabling Reduced‐Temperature HT‐PEMFC Operation (Down to 120°C)

open access: yesAdvanced Energy Materials, EarlyView.
Embedding imidazolium‐biphosphate ion pairs directly into the polybenzimidazole main chain simultaneously stabilizes phosphoric acid and preserves continuous proton transport pathways. Strong ion‐pair and hydrogen‐bond interactions suppress acid leaching while maintaining high conductivity, enabling stable and durable high‐temperature fuel cell ...
Huina Lin, Brian C. Benicewicz
wiley   +1 more source

3D investigation and modeling of the geometric effects on porosity in packed beds

open access: yesAIChE Journal, EarlyView.
Abstract In porous beds, physical boundaries restrict particle arrangement, leading to inhomogeneous porosity. This paper reports on the porosity profiles that are the result of geometric effects on monodisperse packed beds in cylindrical and cubic arrangements. Special focus is given to the influence of edges and corners in cubic geometries.
Bastian Oldach   +3 more
wiley   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

open access: yesAdvanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang   +5 more
wiley   +1 more source

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