Machine learning for advanced functional materials (2023)
- Authors:
- Autor USP: JOSHI, NIRAVKUMAR JITENDRABHAI - IFSC
- Unidade: IFSC
- DOI: 10.1007/978-981-99-0393-1
- Subjects: APRENDIZADO COMPUTACIONAL; ELETROQUÍMICA; SENSOR; INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- ISBN: 9789819903931
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
Machine learning for advanced functional materials. . Singapore: Springer. Disponível em: https://doi.org/10.1007/978-981-99-0393-1. Acesso em: 09 fev. 2026. , 2023 -
APA
Machine learning for advanced functional materials. (2023). Machine learning for advanced functional materials. Singapore: Springer. doi:10.1007/978-981-99-0393-1 -
NLM
Machine learning for advanced functional materials [Internet]. 2023 ;[citado 2026 fev. 09 ] Available from: https://doi.org/10.1007/978-981-99-0393-1 -
Vancouver
Machine learning for advanced functional materials [Internet]. 2023 ;[citado 2026 fev. 09 ] Available from: https://doi.org/10.1007/978-981-99-0393-1 - Functional nanomaterials: advances in gas sensing technologies
- Recent advances on UV-enhanced oxide nanostructures gas sensors
- Hybridized graphitic carbon nitride (g-CN) as high performance VOCs sensor
- Silicon-based hybrid nanoparticles: fundamentals, properties, and applications
- 1D semiconducting hybrid nanostructures: synthesis and applications in gas sensing and optoelectronics
- Nanosensors for monitoring indoor pollution in smart cities
- Machine learning for advanced functional materials. [Prefácio]
- Advances in the designs and mechanisms of MoO3 nanostructures for gas sensors: a holistic review
- Advances and challenges in WO3 nanostructures’ synthesis
- Cellulose acetate-MoS2 nanopetal hybrid: a highly sensitive and selective electrochemical aptasensor of Troponin I for the early diagnosis of Acute Myocardial Infarction
Informações sobre o DOI: 10.1007/978-981-99-0393-1 (Fonte: oaDOI API)
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