Results 271 to 280 of about 76,153 (324)

Prediction of Nontrivial Topological Phases and Rashba Spin-Splitting in BaABTe₄ Janus Monolayers (A, B = Al, Ga, In, or Tl). [PDF]

ACS Omega
D'Souza J, Villaos RAB, Iii ABM, Verzola IMR, Covilakam SP, Huang ZQ, Chuang FC.   +6 more
europepmc   +1 more source

Rethinking Power Solutions for Healthcare Wearables: From Point‐of‐Care and Episodic use to Continuous Monitoring and Therapeutic Platforms

Advanced Energy and Sustainability Research, EarlyView.
This Perspective examines practical power solutions for wearable healthcare systems, highlighting the limits of standard batteries. It categorizes wearables into four domains—point‐of‐care diagnostics, episodic monitoring, continuous long‐term monitoring, and therapeutic platforms—and analyzes their power needs.
Seokheun Choi
wiley   +1 more source

High-performance one-dimensional thermoelectric materials: polyyne chains and their derivatives. [PDF]

Nanoscale Adv
H J K, Bhattacharyya S.
europepmc   +1 more source

Doping in Organic Semiconductors: Fundamentals, Materials, and Applications

Advanced Electronic Materials, EarlyView.
Sergi Riera‐Galindo
wiley   +1 more source

Cadmium and Zinc‐Doped p‐type Sb2Se3 Single Crystals and Solar Cells

Advanced Energy and Sustainability Research, EarlyView.
Cd and Zn were assessed as candidate p‐type dopants in Sb2Se3 single crystals in concentrations between 1016‐1020 cm−3. Both are effective in inducing p‐type conductivity, however Cd doped crystals exhibit lower resistivity across a wider range of dopant levels.
Thomas P. Shalvey, Theodore D. C. Hobson, Frank Herklotz, Reelika Kaupmees, Aeshah A. Almushawwah, Bradley G. Lewis, Christopher H. Don, Sean R. Kavanagh, David O. Scanlon, Vin Dhanak, Maarja Grossberg‐Kuusk, Eduard Lavrov, Ken Durose, Jonathan D. Major   +13 more
wiley   +1 more source

Perspective: Thermophoresis and Its Promise for Optical Patterning. [PDF]

Langmuir
Jiménez Amaya A, Hill EH.
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Modeling magnetic properties of cobalt nanofilms used as a component of spin hybrid superconductor-ferromagnetic structures. [PDF]

Beilstein J Nanotechnol
Fedotov A, Severyukhina O, Salomatina A, Sidorenko A.   +3 more
europepmc   +1 more source

Refractory plasmonic material based floating solar still for simultaneous desalination and electricity generation. [PDF]

iScience
Margeson MJ, Atwood M, Lara de Larrea J, Weatherby JA, Daurie H, Near K, Gagnon GA, Dasog M.   +7 more
europepmc   +1 more source