Results 181 to 190 of about 52,128 (263)

Multiscale Perspectives on Perovskite Instability: From Atomistic Deformation to Device Degradation in Perovskite Solar Cells

Advanced Energy Materials, EarlyView.
Multiscale perspectives on how excess charges in soft lead halide perovskites induce operational instability in photovoltaic devices are presented. Localized carriers formed under thermodynamic non‐equilibrium states modify atomistic interactions and drive lattice distortions, accelerating device degradation.
Joo‐Hong Lee, Donghyeon Lee, Seoyoon Lee, Jin‐Wook Lee, Mansoo Choi, Min‐cheol Kim, Namyoung Ahn   +6 more
wiley   +1 more source

Waste-derived-lignin based hydrogel as a reusable adsorbent for efficient removal of dyes from aqueous solution. [PDF]

RSC Adv
Jannat UT, Haque MA, Aumi SA, Ray SK, Moniruzzaman M, Jahan MS, Ali Shaikh MA, Ehsan MQ.   +7 more
europepmc   +1 more source

Photothermally Enhanced Hydrogel‐Based Thermoelectrochemical Cells for Stable, Passive Harvesting of Low‐Grade Heat

Advanced Energy Materials, EarlyView.
A photothermally enhanced hydrogel‐based thermoelectrochemical cell enables passive conversion of low‐grade heat and solar energy into electricity. The quasi‐solid device achieves high thermopower and stable long‐term operation under passive conditions.
Matteo Bevione, Ziyan Pan, Giulia Tagliabue   +2 more
wiley   +1 more source

Development of a Bioactive Dental Barrier Membrane Based on PCL/Collagen and PVA/Hydroxyapatite Layers with Amoxicillin-Loaded Electrosprayed Coating. [PDF]

Pharmaceutics
Akkus HGN, Bingol AB, Oktay B, Ozsan B, Kızılkurtlu AA, Erarslan A, Ciftci F, Ustündag CB.   +7 more
europepmc   +1 more source

Ultrathin Aluminum–Air Batteries via Hygroscopic Electrolytes for Continuous Operation of Imperceptible On‐Skin Electronics

Advanced Energy Materials, EarlyView.
This study presents ultrathin, high‐capacity aluminum‐air batteries (AABs) for self‐sustaining, skin‐conformal electronics. By utilizing ambient moisture and oxygen alongside a LiCl‐based hygroscopic hydrogel electrolyte, the design significantly reduces battery thickness while preventing dehydration and Al self‐corrosion.
Jaeil Park, Inho Lee, Namhee Kim, Wonseok Shin, Joo Sung Kim, Daegun Kim, Giwon Lee, Jinhan Cho, Hoon‐Hee Ryu, Myung‐Han Yoon, Seung Joon Yoo, Woo‐Jin Song, Sungjun Park   +12 more
wiley   +1 more source

Comparative Study of Plasticized Polyvinyl Alcohol and Hydroxypropyl Methylcellulose Films for Pharmaceutical Applications: Mechanical, Thermal, Structural and Disintegration Properties. [PDF]

Polymers (Basel)
Kurien RA, Kannan G, Krongrawa W, Suttiruengwong S, Sriamornsak P.   +4 more
europepmc   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Aromatic phosphonate-based luminophores: universal building blocks for ultralong room-temperature phosphorescence and multifunctional applications. [PDF]

Chem Sci
Li C, Huang Z, Li T, Zhao T, Zhou L, He Z, Tian H, Ma X.   +7 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source