Results 121 to 130 of about 231,734 (259)

Device Performance of Emerging Photovoltaic Materials (Version 6)

Advanced Energy Materials, EarlyView.
Efficiency of single junction perovskite (circles), organic (hexagons), dye sensitized (pentagons), kesterite (diamonds), Sb2Se3 (right triangle), and AgBiS2 (left triangles) solar cells at emerging‐pv.org over the last decade. ABSTRACT This 6th annual Emerging PV Report surveys peer‐reviewed advances since August 2024 across perovskite, organic ...
Osbel Almora, Agustin O. Alvarez, Derya Baran, Carlos I. Cabrera, Luigi A. Castriotta, Bruno Ehrler, Sule Erten‐Ela, Kenjiro Fukuda, Fei Guo, Jens Hauch, Anita W.Y. Ho‐Baillie, T. Jesper Jacobsson, Rene A. J. Janssen, Thomas Kirchartz, Maria A. Loi, Richard R. Lunt, Xavier Mathew, Jie Min, David B. Mitzi, Mohammad K. Nazeeruddin, Ana F. Nogueira, Ulrich W. Paetzold, Nam‐Gyu Park, Barry P. Rand, Henry Snaith, Takao Someya, Christian Sprau, Lulu Sun, Karen Forberich, Christoph J. Brabec   +29 more
wiley   +1 more source

A Guideline to Evaluate Sorbent Performance for Atmospheric Water Harvesting

Advanced Energy and Sustainability Research, EarlyView.
Reporting guidelines for atmospheric water harvesting sorbents allow benchmarking between research groups across seven key performance indicators (KPIs). Beyond simple equilibrium values (such as capacity or maximum water uptake), these KPIs validate sensitivity, reversibility, regeneration conditions, chemical stability, kinetics, and water quality ...
Simon Ponton, David Brassard, Andrés Felipe Sánchez Rendón, Seyedemad Alavitabari, Richard Boudreault, Emilie Bédard, Pierre‐Luc Girard‐Lauriault, Jason R. Tavares   +7 more
wiley   +1 more source

Spheres, tears, and spears: Regulating the perimeter and circularity of millimeter‐sized alginate hydrogel beads

AIChE Journal, EarlyView.
Abstract Generating hydrogel beads pertains to many engineering applications. We examined two alginate‐based fluids at three concentrations of alginate, cAG$$ {c}_{\mathrm{AG}} $$. We used the “Map of Misery” to determine which material property (viscosity, elasticity, and inertia) drives droplet formation.
Conor G. Harris, Katherine D. Bandettini, Hannah K. Gedde, Lewis Semprini, Kaitlin C. Fogg, Willie E. Rochefort   +5 more
wiley   +1 more source

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

Advanced Intelligent Discovery, EarlyView.
This paper presents a computer vision (deep learning) pipeline integrating YOLOv8 and YOLOv9 for automated detection, segmentation, and analysis of rosette cellulose synthase complexes in freeze‐fracture electron microscopy images. The study explores curated dataset expansion for model improvement and highlights pipeline accuracy, speed ...
Siri Mudunuri, Leala Carbonneau, Eric M. Roberts, Alison W. Roberts, Candace H. Haigler, Alexey V. Gulyuk, Yaroslava G. Yingling   +6 more
wiley   +1 more source

Accelerating Biosensor Discovery: A Computationally‐Driven Pipeline for Microplastics Monitoring

Advanced Intelligent Discovery, EarlyView.
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, Marcos de C. Leite, Pedro H. M. Zanineli, Juliana N. Y. Costa, Débora M. Cunha, Sophia P. Serra, Pedro H. Sophia, Flávio M. Shimizu, Yasmin W. Moura, Célio C. Oliveira, Gabriel R. Schleder, Gabriela F. Persinoti, Leandro Merces, Renato S. Lima   +13 more
wiley   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Building Molecules by a Self‐Replicator That Catalyzes Acyl Hydrazone Formation

Angewandte Chemie, EarlyView.
A self‐replicator efficiently catalyzes the formation of acyl hydrazone bonds between a set of different substrates. In addition to previously reported (bond‐breaking) catalytic activity, this renders the self‐replicator highly promiscuous, which is a good starting point for Darwinian experiments en route to de‐novo life.
Kayleigh S. van Esterik, Tommaso Marchetti, Sijbren Otto   +2 more
wiley   +2 more sources

SuperResNET: Model‐Free Single‐Molecule Network Analysis Software Achieves Molecular Resolution of Nup96

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
SuperResNET is a powerful integrated software that reconstructs network architecture and molecular distribution of subcellular structures from single molecule localization microscopy datasets. SuperResNET segments the nuclear pore complex and corners, extracts size, shape, and network features of all segmented nuclear pores and uses modularity analysis
Yahongyang Lydia Li, Ismail M. Khater, Christian Hallgrimson, Ben Cardoen, Timothy H. Wong, Ghassan Hamarneh, Ivan R. Nabi   +6 more
wiley   +1 more source

Transforming RNA Degradation into Accurate RNA Detection: RNase I–Assisted Rolling Circle Amplification for Clinical Samples

Angewandte Chemie, EarlyView.
Here, we transform this otherwise destructive enzymatic activity into a powerful diagnostic advantage through an RNase I–assisted rolling circle amplification (RI‐RCA) strategy. By integrating controlled RNase I–mediated RNA digestion with circular DNA templates, this approach enables direct and highly sensitive detection of target RNA sequences. Using
Amal Mathai, Cheng‐Yu Lee, Zijie Zhang, Jimmy Gu, John D. Brennan, Yingfu Li   +5 more
wiley   +2 more sources

Cross‐Modal Characterization of Thin‐Film MoS2 Using Generative Models

Advanced Intelligent Systems, EarlyView.
Cross‐modal learning is evaluated using atomic force microscopy (AFM), Raman spectroscopy, and photoluminescence spectroscopy (PL) through unsupervised learning, regression, and autoencoder models. Autoencoder models are used to generate spectroscopy data from the microscopy images.
Isaiah A. Moses, Chen Chen, Joan M. Redwing, Wesley F. Reinhart   +3 more
wiley   +1 more source