Results 211 to 220 of about 9,408,729 (324)

Deciphering the effect of synthesis methods on the thermoelectric properties of ZnO nanostructures

open access: gold
Shivam K. Singh   +8 more
openalex   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

open access: yesAdvanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen   +4 more
wiley   +1 more source

Ultrathin Aluminum–Air Batteries via Hygroscopic Electrolytes for Continuous Operation of Imperceptible On‐Skin Electronics

open access: yesAdvanced Energy Materials, EarlyView.
This study presents ultrathin, high‐capacity aluminum‐air batteries (AABs) for self‐sustaining, skin‐conformal electronics. By utilizing ambient moisture and oxygen alongside a LiCl‐based hygroscopic hydrogel electrolyte, the design significantly reduces battery thickness while preventing dehydration and Al self‐corrosion.
Jaeil Park   +12 more
wiley   +1 more source

Thermoradiative Diodes Using Black Phosphorus van der Waals Materials

open access: yesAdvanced Energy Materials, EarlyView.
We demonstrate thermoradiative diodes based on black phosphorus (bP${\rm bP}$)/molybdenum disulfide (MoS2$MoS_2$) van der Waals heterojunctions which generate power by exchanging photons with a colder environment. The bandgap of black phosphorus is well positioned to exchange photons with outer space through the mid‐wave atmospheric window.
Yuchen Sun   +13 more
wiley   +1 more source

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

open access: yesAdvanced 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   +5 more
wiley   +1 more source

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