Results 91 to 100 of about 968 (169)

Three‐dimensional Antimony Sulfide Based Flat Optics

Advanced Functional Materials, EarlyView.
This work presents the development of a grayscale electron beam lithography (g‐EBL) method for fabricating antimony trisulfide (Sb2S3) nanostructures with customizable 3D profiles. The refractive index of g‐EBL patterned Sb2S3 is determined based on the synergy of genetic algorithm and transfer matrix method.
Wei Wang, Uwe Hübner, Anne Gärtner, Tao Chen, Joseph Köbel, Franka Jahn, Daniel Repp, Abhrodeep Dey, Henrik Schneidwind, Andrea Dellith, Jan Dellith, Torsten Wieduwilt, Matthias Zeisberger, Tanveer Ahmed Shaik, Astrid Bingel, Markus A. Schmidt, Jer‐Shing Huang, Ulf Peschel, Volker Deckert   +18 more
wiley   +1 more source

Symbolic metaprogram search improves learning efficiency and explains rule learning in humans. [PDF]

Nat Commun
Rule JS, Piantadosi ST, Cropper A, Ellis K, Nye M, Tenenbaum JB.   +5 more
europepmc   +1 more source

Single Solid‐State Ion Channels as Potentiometric Nanosensors

Advanced Functional Materials, EarlyView.
Single gold nanopores functionalized with mixed self‐assembled monolayers act as solid‐state ion channels for direct, selective potentiometric sensing of inorganic ions (Ag⁺). The design overcomes key miniaturization barriers of conventional ion‐selective electrodes by combining low resistivity with suppressed loss of active components, enabling robust
Gergely T. Solymosi, Gyula Jágerszki, Levente Illés, Péter Fürjes, Róbert E. Gyurcsányi   +4 more
wiley   +1 more source

A Van der Waals Optoelectronic Synapse with Tunable Positive and Negative Post‐Synaptic Current for Highly Accurate Spiking Neural Networks

Advanced Functional Materials, EarlyView.
A van der Waals optoelectronic synaptic device based on a ReS2/WSe2 heterostructure and oxygen‐treated h‐BN is presented, which enables both positive and negative PSCs through photocarrier polarity reversal. Bidirectional plasticity arises from gate‐tunable band bending and charge trapping‐induced quasi‐doping.
Hyejin Yoon, Soeun Park, Yeong Kwon Kim, Juhwan Baek, Ki Han Kim, Seongil Yun, Hyeonchang Son, Jeongeun Choi, Byung Chul Jang, Dong‐Ho Kang   +9 more
wiley   +1 more source

Spatial and temporal modeling of conflict related fatality and public health implications in Nigeria. [PDF]

Sci Rep
Egbon OA, Belachew AM, Bogoni MA, Gayawan E, Louzada F.   +4 more
europepmc   +1 more source

A Tracer Diffusion Study of Diverse Photo‐Ionic Phenomena in Strontium Titanate

Advanced Functional Materials, EarlyView.
Two strong interfacial photo‐ionic effects are demonstrated for the model system SrTiO3 through the application of isotope exchange experiments: UV illumination is found to enhance the oxygen surface exchange coefficient by several orders of magnitude and to depress the surface space‐charge potential substantially.
David M. Schwenkel, Timon F. Kielgas, George F. Harrington, Roger A. De Souza   +3 more
wiley   +1 more source

Navigating chemical space: multi-level Bayesian optimization with hierarchical coarse-graining. [PDF]

Chem Sci
Walter LJ, Bereau T.
europepmc   +1 more source

Integrative Approaches for DNA Sequence‐Controlled Functional Materials

Advanced Functional Materials, EarlyView.
DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo, Ewa Anna Oprzeska‐Zingrebe, Mariana Kozlowska, Lennart Hilbert, Iliya D. Stoev   +4 more
wiley   +1 more source

Predicting reaction conditions: a data-driven perspective. [PDF]

Chem Sci
Ball M, Horvath D, Kogej T, Kabeshov M, Varnek A.   +4 more
europepmc   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source