Results 221 to 230 of about 291,764 (285)

Research Progress and Applications of Non‐Carrier‐Injection Electroluminescence

Advanced Electronic Materials, EarlyView.
Non‐carrier‐injection electroluminescence (NCI‐EL) uses AC fields and displacement currents to trigger light from internal charge reservoirs, enabling minimalist emitters with remotely coupled terminals. This review maps shared mechanisms across organics, GaN, quantum dots, and TMDCs, compares planar, interdigital, single‐terminal, and coaxial designs,
Wei Huang, Jianbi Le, Rong Chen, Zhixian Lin, Xiongtu Zhou, Yongai Zhang, Chaoxing Wu   +6 more
wiley   +1 more source

Gallery Architecture and Reproductive Strategy of <i>Ips hauseri</i> (Coleoptera: Curculionidae) in a <i>Picea schrenkiana</i> Forest: Implications for Population Dynamics Under Outbreak Conditions. [PDF]

Insects
Fan Y, Dai L, Gao H.
europepmc   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Storage and Export of Atmospheric Hg, Pb, Al, and Fine Particulate Matter (PM2.5) from Forest Trees. [PDF]

Environ Sci Technol
Landis JD.
europepmc   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Carbon stock quantification and climate mitigation potential of a tropical moist forest in Ethiopia. [PDF]

PLoS One
Shembo AK, Soromessa T, Demissew S, Geremew A, Ray RL, Carson L.   +5 more
europepmc   +1 more source

Roadmap for High‐Throughput Ceramic Materials Synthesis and Discovery for Batteries

Advanced Energy Materials, EarlyView.
This work examines ceramic synthesis through the lens of high‐throughput synthesis and optimization, identifying opportunities for faster, adaptable routes. It emphasizes flexible liquid precursor–to–solid film methods over slower solid‐state approaches and highlights computer‐aided decision making to optimize both material properties and device ...
Jesse J. Hinricher, Katarzyna P. Sokol, Philipp Simons, Kun Joong Kim, Michael Foshey, Yunsheng Tian, Thorben Prein, Lincoln J. Miara, Elsa Olivetti, Wojciech Matusik, Jennifer L. M. Rupp   +10 more
wiley   +1 more source

Intelligent routing for human activity recognition in wireless body area networks. [PDF]

Sci Rep
Elmosallamy ES, Soliman MF.
europepmc   +1 more source

Inversion of the Impedance Response Towards Physical Parameter Extraction Using Interpretable Machine Learning

Advanced Energy Materials, EarlyView.
ABSTRACT Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift‐diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible.
Mahmoud Nabil, Isel Grau, Ricardo Grau‐Crespo, Said Hamad, Juan A. Anta   +4 more
wiley   +1 more source

Effect of Thinning on Tree Differentiation, Productivity and Carbon Stocks of <i>Cryptomeria japonica</i> Plantations. [PDF]

Ecol Evol
Liu K, Chen B, Zhou P, Zhang B, Wang R, Wang C.   +5 more
europepmc   +1 more source