Automated Discovery of Therapeutic Biomaterial for Renally Impaired Hyperuricemia Patients by Natural Language Processing and Machine Learning DOI Creative Commons
Xiaodong Zeng, Jiahao Qiu,

Xin Zhao

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

The exponential growth of scientific publications presents opportunities for researchers to identify valuable knowledge, especially in the highly interdisciplinary field --- biomaterials, where exploiting possible connections between unmet clinical needs and materials properties from literatures is crucial. However, with traditional literature reading, it extremely challenging marry existing reported different applications or other purposes. Here, provide a not-renally cleared therapeutics renally impaired hyperuricemia patients, we designed multi-tiered framework MatWISE that fuses state-of-the-art natural language processing, semantic relationship mapping, machine learning automate complex process material discovery sea published until December 2022, successfully identified optimized δ-MnO 2 into an orally administered, nonabsorbable uric acid (UA) lowering biomaterial. had superior serum urine UA-lowering effect three mouse models, by comparing standard care drug. promising serve as safe effective drug patients. We demonstrated new research paradigm biomaterials combining techniques handful experiments discover translationally relevant massive research, need.

Язык: Английский

Artificial intelligence artificial muscle of dielectric elastomers DOI Creative Commons
Dongyang Huang, Jiaxuan Ma, Yubing Han

и другие.

Materials & Design, Год журнала: 2025, Номер unknown, С. 113691 - 113691

Опубликована: Фев. 1, 2025

Процитировано

0

Demonstration of NiCo as an Alternative Metal for Post-Cu Interconnects DOI

Ju Young Sung,

Gi-Young Jo,

Sang Mo Moon

и другие.

ACS Nano, Год журнала: 2025, Номер unknown

Опубликована: Фев. 12, 2025

Copper (Cu) is currently the dominant interconnect material in back-end-of-line processes due to its low bulk resistivity (ρ0). Unfortunately, increases significantly at small dimensions a long electron mean free path (EMFP, λ) of 39 nm, which leads enhanced scattering grain boundaries and surfaces, thereby limiting high-density integration. In this study, we demonstrate that single-phase hexagonal-close-packed (HCP) nickel–cobalt (NiCo) alloy thin films exhibit reduced size effect, outperforming Cu for thicknesses below 8 nm (19.83 μΩ·cm 4.9 nm). First-principles calculations predicted lower ρ0·λ 5.68 × 10–16 Ω m2 HCP NiCo (xx) than (6.73 m2), significant contribution from short EMFP approximately 5 alloy. To date, growth film had not been achieved, as both face-centered-cubic (FCC) phases coexist Co concentration 50–80 atom %. However, were successfully grown by using an seed layer, lattice mismatch with sapphire (0001) substrate. Moreover, can be dry etched, providing advantage device fabrication, since it does require damascene process unlike Cu. The also exhibited high thermal stability above 500 °C. Therefore, considered promising alternative overcome scaling limitations interconnects.

Язык: Английский

Процитировано

0

Machine‐Learning‐Enhanced Trial‐and‐Error for Efficient Optimization of Rubber Composites DOI Open Access
Wei Deng, Lijun Liu, Xiaohang Li

и другие.

Advanced Materials, Год журнала: 2025, Номер unknown

Опубликована: Март 10, 2025

The traditional trial-and-error approach, although effective, is inefficient for optimizing rubber composites. latest developments in machine learning (ML)-assisted methodologies are also not suitable predicting and composite properties. This due to the dependency of properties on processing conditions, which prevents alignment data collected from different sources. In this work, a novel workflow called ML-enhanced approach proposed. integrates orthogonal experimental design with symbolic regression (SR) effectively extract empirical principles. combination enables optimization process retain characteristics while significantly improving efficiency capability. Using composites as model system, extracts principles encapsulated by high-frequency terms SR-derived mathematical formulas, offering clear guidance material property optimization. An online platform has been developed that allows no-code usage proposed methodology, designed seamlessly integrate into existing process.

Язык: Английский

Процитировано

0

How Do Organic Batteries Work? Theoretical and Design Principles of Electrode Materials for All‐Organic Batteries DOI Creative Commons
Robin Weßling, Philipp Penert, Birgit Esser

и другие.

Advanced Energy Materials, Год журнала: 2025, Номер unknown

Опубликована: Фев. 24, 2025

Abstract Post‐Li battery technologies are becoming increasingly important. The diverse range of electrically powered devices requires a diversification electrochemical energy storage technologies. Organic electrode materials particular importance for alternative batteries, not only because the natural abundance their constituting elements and low toxicity, but also operating principle redox reactions compatibility with many types chemistries, including multivalent metal anionic batteries. All‐organic batteries still “young” field research offer promising opportunities in terms mechanical processing properties. In development using organic understanding mechanisms, different cell correct interpretation data is utmost importance. This comprehensive review offers insight into working organic‐based material design considerations, structure‐property relations, highlighting standardized terminology, characterization newly developed cells, distinguishing between half‐cells full‐cells.

Язык: Английский

Процитировано

0

Automated Discovery of Therapeutic Biomaterial for Renally Impaired Hyperuricemia Patients by Natural Language Processing and Machine Learning DOI Creative Commons
Xiaodong Zeng, Jiahao Qiu,

Xin Zhao

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

The exponential growth of scientific publications presents opportunities for researchers to identify valuable knowledge, especially in the highly interdisciplinary field --- biomaterials, where exploiting possible connections between unmet clinical needs and materials properties from literatures is crucial. However, with traditional literature reading, it extremely challenging marry existing reported different applications or other purposes. Here, provide a not-renally cleared therapeutics renally impaired hyperuricemia patients, we designed multi-tiered framework MatWISE that fuses state-of-the-art natural language processing, semantic relationship mapping, machine learning automate complex process material discovery sea published until December 2022, successfully identified optimized δ-MnO 2 into an orally administered, nonabsorbable uric acid (UA) lowering biomaterial. had superior serum urine UA-lowering effect three mouse models, by comparing standard care drug. promising serve as safe effective drug patients. We demonstrated new research paradigm biomaterials combining techniques handful experiments discover translationally relevant massive research, need.

Язык: Английский

Процитировано

0