Results 191 to 200 of about 136,820 (296)

A Topology Optimization Framework for the Inverse Design of Nonlinear Mechanical Metamaterials

Advanced Engineering Materials, EarlyView.
This work uses topology optimization to design unit cells for mechanical metamaterials with a prescribed nonlinear stress–strain response. The framework adds contact and postbuckling modeling to synthesize microstructures for three highly nonlinear responses, including pseudoductile behavior, monostable with snap‐through buckling, and bistable ...
Charlie Aveline, Matthew Santer, Robert Hewson   +2 more
wiley   +1 more source

Optimization based load forecasting and demand management in smart building microgrids with Greylag Goose and Bi level graph models. [PDF]

Sci Rep
Ahamed BS, Dhanya D, Sivaramkrishnan M, Srinivas TA, Hadi AA, Khan S, Khan WA.   +6 more
europepmc   +1 more source

Bias‐Free Highly Efficient and Stable Dye‐Sensitized Photoelectrochemical Cells via Cascade Charge Transfer

Advanced Functional Materials, EarlyView.
A buried‐junction DSPEC design is introduced that leverages cascade charge transfer to enhance efficiency, stability, and versatility. This approach facilitates effective charge transfer and minimizes recombination losses, leading to significant improvements.
Jun‐Hyeok Park, Kyounglim Kim, Jinyoung Lee, Sarang Kim, Gwangsu Yoon, So Yeon Yoon, Jeong Kyeong Lee, Ji‐Wook Jang, Tae‐Hyuk Kwon   +8 more
wiley   +1 more source

UniEload: Electrical load dataset for energy forecasting applications at public universities in Bangladesh. [PDF]

Data Brief
Sutradhar U, Biswas P, Hossain S, Islam ATMS, Dev S.   +4 more
europepmc   +1 more source

Printed Integrated Logic Circuits Based on Chitosan‐Gated Organic Transistors for Future Edible Systems

Advanced Functional Materials, EarlyView.
Edible electronics needs integrated logic circuits for computation and control. This work presents a potentially edible printed chitosan‐gated transistor with a design optimized for integration in circuits. Its implementation in integrated logic gates and circuits operating at low voltage (0.7 V) is demonstrated, as well as the compatibility with an ...
Giulia Coco, Valerio Francesco Annese, Valerio Galli, Ilaria Penna, Debora Russo, Marina Veronesi, Rita Scarpelli, Stefania Sabella, Mario Caironi   +8 more
wiley   +1 more source

Positive‐Tone Nanolithography of Antimony Trisulfide with Femtosecond Laser Wet‐Etching

Advanced Functional Materials, EarlyView.
A butyldithiocarbamic acid (BDCA) etchant is used to fabricate various micro‐ and nanoscale structures on amorphous antimony trisulfide (a‐Sb2S3) thin film via femtosecond laser etching. Numerical analysis and experimental results elucidate the patterning mechanism on gold (reflective) and quartz (transmissive) substrates.
Abhrodeep Dey, Uwe Hübner, Albane Benardais, Xiaofei Wu, Andrea Dellith, Jan Dellith, Torsten Wieduwilt, Henrik Schneidewind, Markus A Schmidt, Virginie Nazabal, Volker Deckert, Jer‐Shing Huang, Wei Wang   +12 more
wiley   +1 more source

A multi strategy optimization framework using AI digital twins for smart grid carbon emission reduction. [PDF]

Sci Rep
Sakthivel S, Arivukarasi M, Charulatha G, Nithisha J, Abirami B, Jaithunbi AK, Suresh Kumar V.   +6 more
europepmc   +1 more source

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley   +1 more source

Correlation based feature importance analysis for improving machine learning stability predictions in hybrid PV systems. [PDF]

Sci Rep
Swarnkar V, Ralhan S, Singh M, Parashar D, Singh M.   +4 more
europepmc   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source