Results 151 to 160 of about 1,734,839 (338)

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +1 more source

Conditional Independent Test in the Presence of Measurement Error with Causal Structure Learning [PDF]

Zine el abidine Kherroubi, Fouzi Boukhalfa, Thierry Lestable, Carlos-Faouzi Bader, Zhifeng Hao, Zhengming Chen   +5 more
openalex   +1 more source

Dynamic Control of Synaptic Plasticity by Competing Ferroelectric and Trap‐Assisted Switching in IGZO Transistors with Al2O3/HfO2 Dielectrics

Advanced Functional Materials, EarlyView.
A frequency‐tunable ferroelectric synaptic transistor based on a buried‐gate InGaZnO channel and Al2O3/HfO2 dielectric stack exhibits linear and reversible weight updates using single‐polarity pulses. By switching between ferroelectric and trap‐assisted modes depending on input frequency, the device simplifies neuromorphic circuit design and achieves ...
Ojun Kwon, Dong Ho Lee, Seyoung Oh, Jongwon Yoon, Hyo‐Bae Kim, Ji‐Hoon Ahn, Woojin Park, Han Seul Kim, Byungjin Cho   +8 more
wiley   +1 more source

Miniature Nanomesh Mechano‐Acoustic Sensor with Wide Linear Dynamic Range, Broad Bandwidth, and Flat Frequency Response

Advanced Functional Materials, EarlyView.
A miniaturized mechano‐acoustic sensor is developed using an electrospun PVDF nanomesh as the diaphragm in a capacitive sensor structure. Unlike conventional nanomesh‐based sensors, it achieves high linear sensitivity, a broad and flat frequency response, and a compact form factor.
Jeng‐Hun Lee, Kang Hyuk Cho, Seojin Yun, Siyoung Lee, Woongji Kim, Yunsik Kim, Wonkyu Moon, Yoonyoung Chung, Kilwon Cho   +8 more
wiley   +1 more source

Dual-Mode Encryption for UC-Secure String OT from Learning with Errors

IET Information Security
Universal composability (UC) is a primary security flavor for designing oblivious transfer (OT) due to its advantage of arbitrary composition. However, the study of UC-secure OT over lattices is still far behind compared with constructions over ...
Momeng Liu, Yupu Hu, Qiqi Lai, Shanshan Zhang, Huiwen Jia, Wen Gao, Baocang Wang   +6 more
doaj   +1 more source

Deterministic response strategies in trial-and-error learning

, 2018
Holger Mohr, Katharina Zwosta, Dimitrije Marković, Sebastian Bitzer, Uta Wolfensteller, Hannes Ruge   +5 more
openalex   +1 more source

Functional Materials for Environmental Energy Harvesting in Smart Agriculture via Triboelectric Nanogenerators

Advanced Functional Materials, EarlyView.
This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva, Giulio Fatti, Emanuel Carlos, Guilherme Ferreira, Sumita Goswami, Suman Nandy, Luiz H. C. Mattoso, Elvira Fortunato, Caio G. Otoni, Rodrigo Martins   +9 more
wiley   +1 more source

TAPAS: Datasets for Learning the Learning with Errors Problem

AI-powered attacks on Learning with Errors (LWE), an important hard math problem in post-quantum cryptography, rival or outperform "classical" attacks on LWE under certain parameter settings. Despite the promise of this approach, a dearth of accessible data limits AI practitioners' ability to study and improve these attacks.
Saxena, Eshika, Alfarano, Alberto, Charton, François, Wenger, Emily, Lauter, Kristin   +4 more
openaire   +2 more sources

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

Pearce-Hall error learning theory

, 2008
Geoffrey Hall
openalex   +1 more source