Results 61 to 70 of about 184,822 (218)

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

DigiChrom: A Domain Ontology for Semantic Representation of Trivalent Chromium Platings and Its Large Language Model‐Based Alignment With Multiple Mid‐Level Ontologies

Advanced Engineering Materials, EarlyView.
Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter, Elena Garcia Trelles, Christoph Schweizer, Thomas Seifert, Christoph Baumer, Mahmoud Elkady, Katja Feige, Daniel Grimme, Edgar Hoffmann, Andreas Waibel, Anke Walter   +10 more
wiley   +1 more source

Optoelectronic Synaptic Devices Using Molecular Telluride Phase‐Change Inks for Three‐Factor Learning

Advanced Functional Materials, EarlyView.
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner, Dhananjeya Kumaar, Giacomo Torti, Killian Keller, Till Zellweger, Florian M. Schenk, Christoph Weilenmann, Daniel Chelladurai, Vara Prasad Jonnalagadda, Ghazi Sarwat Syed, Vanessa Wood, Jürg Leuthold, Alexandros Emboras, Maksym Yarema, Mathieu Luisier   +14 more
wiley   +1 more source

Achieving High ON State Current through Ferroelectric Polarization‐Dependent Interfacial Resistance Switching in Undoped Orthorhombic HfO2 Films

Advanced Functional Materials, EarlyView.
Ferroelectric tunnel junction devices based on epitaxial undoped ferroelectric HfO2 films demonstrate stable switching endurance of over 106 switching cycles, low write voltages of ±3 V, 16 measured resistance states, and neuromorphic capability.
Markus Hellenbrand, Nuno Estrócio, Ji S. Kim, Babak Bakhit, Marian C. Istrate, Corneliu Ghica, Tiago Rebelo, Nives Strkalj, Abin Varghese, Bernardo Almeida, Luís S. Marques, Bipin Rajendran, Judith L. MacManus‐Driscoll, José P. B. Silva   +13 more
wiley   +1 more source

Advances in Sustainable and Wearable Textile Based Soft Robotics

Advanced Functional Materials, EarlyView.
This Review examines advances in wearable textile‐based soft robotics, focusing on sustainable materials, integrated sensing, and scalable actuation. It discusses manufacturing and system integration across healthcare, assistive robotics, prosthetics, and human–machine interfaces, and highlights key challenges in circular design, including life‐cycle ...
Zahir Abbas, Marzia Dulal, Sophia K. Novoselov, Victoria K. Novoselov, Kostya S. Novoselov, Shaila Afroj, Nazmul Karim   +6 more
wiley   +1 more source

Integrating longitudinal mental health data into a staging database: harnessing DDI-lifecycle and OMOP vocabularies within the INSPIRE Network Datahub

Frontiers in Big Data
BackgroundLongitudinal studies are essential for understanding the progression of mental health disorders over time, but combining data collected through different methods to assess conditions like depression, anxiety, and psychosis presents significant ...
Bylhah Mugotitsa, Bylhah Mugotitsa, Tathagata Bhattacharjee, Michael Ochola, Dorothy Mailosi, David Amadi, Pauline Andeso, Joseph Kuria, Reinpeter Momanyi, Evans Omondi, Evans Omondi, Dan Kajungu, Jim Todd, Agnes Kiragga, Agnes Kiragga, Jay Greenfield   +15 more
doaj   +1 more source

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Journal of Cardiovascular Magnetic Resonance, 2019
Background Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction.
Alex Bratt, Jiwon Kim, Meridith Pollie, Ashley N. Beecy, Nathan H. Tehrani, Noel Codella, Rocio Perez-Johnston, Maria Chiara Palumbo, Javid Alakbarli, Wayne Colizza, Ian R. Drexler, Clerio F. Azevedo, Raymond J. Kim, Richard B. Devereux, Jonathan W. Weinsaft   +14 more
doaj   +1 more source

Tuning Redox Dynamics in Cu‐Based Electrocatalysts: Effect of Secondary Metals

Advanced Functional Materials, EarlyView.
Advanced analysis of operando X‐ray absorption spectroscopy data reveals that secondary metals affect the redox processes in Cu‐based catalysts for electrocatalytic CO2 reduction. This influences the amount of oxides formed under pulsed reaction conditions and the distribution of reaction products.
Martina Rüscher, Andrea Martini, Eduardo Ortega, Lichen Bai, Eric Liberra, Daniel Escalera‐López, Hyo Sang Jeon, Antonia Herzog, Clara Rettenmaier, Dorottya Hursán, David Koshy, Mariana Cecilio de Oliveira Monteiro, Timon Wagner, Leon Jacobse, Felix Haase, Arno Bergmann, Janis Timoshenko, Beatriz Roldan Cuenya   +17 more
wiley   +1 more source

On Leveraging Machine Learning in Sport Science in the Hypothetico-deductive Framework

Sports Medicine - Open
Supervised machine learning (ML) offers an exciting suite of algorithms that could benefit research in sport science. In principle, supervised ML approaches were designed for pure prediction, as opposed to explanation, leading to a rise in powerful, but ...
Jordan Rodu, Alexandra F. DeJong Lempke, Natalie Kupperman, Jay Hertel   +3 more
doaj   +1 more source