Results 211 to 220 of about 84,148 (281)
Hybrid Heterostructures of Nanostructured Si Membrane and MoS2 for Self‐Powered Photodetectors
A scalable MoS2/Si nanomesh vertical heterostructure is developed for self‐powered photodetection. The nanostructured junction enhances conductivity, expands the active heterointerface, and enables a transition from photovoltaic to avalanche‐assisted operation, delivering markedly improved photoresponse over planar counterparts.
Jingying Liu +8 more
wiley +1 more source
Fluorinated tetraphenylethene (TPE) derivatives were developed as interfacial layers for perovskite solar cells. The optimized TFP‐TPE enhances interfacial compatibility, suppresses molecular aggregation, and improves charge extraction. Devices incorporating TFP‐TPE achieved a record efficiency of 25.29% and outstanding stability under humidity and ...
Xiao‐Xin Gao +13 more
wiley +1 more source
A simple solution‐based ITO surface treatment unlocks the full potential of phosphonic‐acid SAMs by balancing surface hydroxylation, conductivity, and homogeneity. Moderately hydroxylated interfaces yield uniform, electronically favourable contacts, enhancing charge extraction, stability, and thermal‐cycling resilience, with broad applicability across ...
Rik Hooijer +18 more
wiley +1 more source
We identify two decisive levers for SAM interfaces: molecular design (carboxylic acid‐based, phosphonic acid, other anchoring chemistries, and polymeric SAMs) and mixing routes (co‐assembly, in situ assembly, pre‐ and post‐treatment). Coordinated tuning of headgroups and assembly pathways optimises energy alignment and film formation, suppresses ...
Jiaxu Zhang, Bochun Kang, Feng Yan
wiley +1 more source
Organic photoelectrochemical cells based on π‐conjugated semiconductors offer a versatile platform for solar fuel generation. This review outlines operating principles, device architectures, and key metrics, and highlights advances in p‐ and n‐type photoelectrodes, interfacial engineering, and catalyst integration.
Jaehyeong Kim +8 more
wiley +1 more source
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
Accelerating Biosensor Discovery: A Computationally‐Driven Pipeline for Microplastics Monitoring
A computationally guided pipeline unites molecular simulation, synthetic biology, electrochemical engineering, and machine learning to accelerate biosensor discovery. A Bacillus anthracis carbohydrate‐binding module is used to develop a high‐performance micro‐ and nanoplastics sensor with greatly reduced error and variability.
Gabriel X. Pereira +13 more
wiley +1 more source
Smart Bioinspired Material‐Based Actuators: Current Challenges and Prospects
This work gathers, in a review style, an extensive and comprehensive literature overview on the development of autonomous actuators based on synthetic materials, bringing together valuable knowledge from several studies. Furthermore, the article identifies the fundamental principles of actuation mechanisms and defines key parameters to address the size
Alejandro Palacios +4 more
wiley +1 more source
Stable Synapse‐Like Memory Switching in N‐Heterocyclic Carbene Monolayers
We report a redox‐active N‐heterocyclic carbene (NHC) monolayer showing synapse‐like behavior via proton‐coupled electron transfer (PCET). These quinone‐functionalized NHCs form dense self‐assembled monolayers and highly stable molecular junctions. Bias‐driven PCET switches quinone/hydroquinone states, producing reversible hysteresis and spike‐timing ...
Ankita Das +11 more
wiley +2 more sources
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy +2 more
wiley +1 more source

