Results 301 to 310 of about 8,478,831 (374)

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

The electronic structure, optical, and thermoelectric properties of novel Bi₂PbCh₄ (Ch = Se, Te) materials: insights from first-principles study. [PDF]

RSC Adv
Al-Hmoud MMA, Gul B, Salman Khan M, Aziz SM, Ullah Z, Benabdellah G, Binzowaimil AM.   +6 more
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

Electronic, optical, magnetocaloric, and thermoelectric properties of La_0.5Sm_0.2Sr_0.3MnO₃: a first-principles DFT-MFT investigation. [PDF]

RSC Adv
Mabrouki AE, Abdouli K, Bardaoui A, Messaoudi O, Alfhaid L, Aljaloud AS.   +5 more
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

Dislocation Introduction via Domain Engineering in Mg₂Sn Single Crystal to Improve its Thermoelectric Properties. [PDF]

Small Methods
Huang Z, Hayashi K, Saito W, Li H, Pei J, Dong J, Chiba T, Nan X, Zhang BP, Li JF, Miyazaki Y.   +10 more
europepmc   +1 more source

Solid-State Synthesis and Thermoelectric Properties of CuFeSe₂-CuFeS₂ Solid Solutions. [PDF]

Materials (Basel)
Jang SM, Kim IH.
europepmc   +1 more source

Thermoelectric Properties of Cu₂S Doped with P, As, Sb and Bi-Theoretical and Experimental Studies. [PDF]

Materials (Basel)
Nieroda P, Leszczyński J, Kapera K, Rutkowski P, Ziewiec K, Szymańska A, Kruszewski MJ, Rudnik M, Koleżyński A.   +8 more
europepmc   +1 more source

Unleashing the Impact of Topological Surface States on the Thermoelectric Properties of Granular Sb₂Te₃ Thin Films Deposited on Flexible Substrates. [PDF]

ACS Appl Mater Interfaces
Locatelli L, Rossi P, Kumar A, Wiemer C, Lamperti A, Mantovan R, Raciti G, Xu K, Reparaz JS, Caironi M, Pace G.   +10 more
europepmc   +1 more source