Results 161 to 170 of about 12,572 (263)

Toward a Rational Design of Conjugated Copolymers with Oxygenated Side Chains for Boosting Thermoelectric Properties

Advanced Energy Materials, EarlyView.
The molecular design strategy that integrates both side chain and backbone engineering in diketopyrrolopyrrole‐based conjugated polymers to identify the optimal balance between doping efficiency and microstructural order is demonstrated. Comprehensive spectroscopic, electrochemical, morphological, and structural characterizations reveal that the ...
Taewoong Han, Suhui Sim, Hoimin Kim, Taewoong Yoon, Hyungju Ahn, Seungjoo Chang, Namgyu Park, Hajeong Kim, Shabaz Alam, Dongki Lee, Sungjun Park, Sungjoo Lee, Jaewon Lee, Boseok Kang   +13 more
wiley   +1 more source

Fast Thermoelectric Responses from Unconventional Na-I Stoichiometry in Reduced Graphene Oxide Films. [PDF]

Adv Sci (Weinh)
Xia X, Luo W, Wang T, Zhang Y, Jiang J, Li P, Mu L, Chen L, Tu Y.   +8 more
europepmc   +1 more source

Dual Band Optimization and Defect Engineering Enable Ultrahigh Thermoelectric Performance in Filled Skutterudites

Advanced Energy Materials, EarlyView.
A synergistic dual‐modulation strategy is employed in Yb‐filled skutterudites through concurrent indium filling and tellurium doping to optimize electronic band structure and phonon scattering. Cooperative band convergence and nanoprecipitate‐induced lattice distortions yield a high zT of 1.72 and enable an ultra‐efficient thermoelectric device with 8 ...
Xuri Rao, Jianrui Wang, Xiao‐Bo Tan, Rui‐Heng Li, Huangshui Ma, Yu Yin, Fan Feng, Pingan Song, Min Hong, Jiaqing He, Ran Ang   +10 more
wiley   +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

Reduced Lattice Thermal Conductivity in Thermoelectric α-MgAgSb via Sb₂Te₃ Powder Atomic Layer Deposition. [PDF]

ACS Appl Mater Interfaces
García Santamaría I, Bahrami A, Wrzesińska-Lashkova A, Charvot J, Sotnikov A, Giebeler L, Vaynzof Y, Bureš F, Ying P, Nielsch K.   +9 more
europepmc   +1 more source

Toward Environmentally Friendly Hydrogel‐Based Flexible Intelligent Sensor Systems

Advanced Intelligent Discovery, EarlyView.
This review summarizes environmentally and biologically friendly hydrogel‐based flexible sensor systems focusing on physical, chemical, and physiological sensors. Furthermore, device concepts moving forward for the practical application are discussed about wireless integration, the interface between hydrogel and dry electronics, automatic data analysis
Sudipta Kumar Sarkar, Kuniharu Takei
wiley   +1 more source

Nanotwin architecture and ultra-high valley degeneracy lead to high thermoelectric performance in GeTe-based thermoelectric materials. [PDF]

Nat Commun
Li S, Yang Y, Fei X, Geng Y, Nan J, Peng P, Li G, Zhang Y, Liu X, Zhang Y, Wu H, Tang G.   +11 more
europepmc   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

A waterproof and ultra-elastic thermoelectric foam for underwater human signal detection. [PDF]

Nat Commun
Liu W, Shi XL, He X, Zhu S, Li Z, Li C, Zhang D, Li M, Wu X, Zhang H, Wang L, Qin X, Chen ZG.   +12 more
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source