In chemico methodology for engineered nanomaterial categorization according to number, nature and oxidative potential of reactive surface sites DOI Creative Commons
Víctor Alcolea-Rodriguez, Raquel Portela, Vanesa Calvino Casilda

et al.

Environmental Science Nano, Journal Year: 2024, Volume and Issue: 11(9), P. 3744 - 3760

Published: Jan. 1, 2024

Methanol probe chemisorption quantifies the number of reactive sites at surface engineered nanomaterials, enabling normalization per site in reactivity and toxicity tests, rather than mass or physical area. Subsequent temperature-programmed reaction (TPSR) chemisorbed methanol identifies nature (acidic, basic, redox combination thereof) their reactivity. Complementary to assay, a dithiothreitol (DTT) oxidation is used evaluate capacity. These acellular approaches quantify number, nature, constitute new approach methodology (NAM) for site-specific classification nanomaterials. As proof concept, CuO, CeO2, ZnO, Fe3O4, CuFe2O4, Co3O4 two TiO2 nanomaterials were probed. A harmonized descriptor ENMs was obtained: DTT rate site, oxidative turnover frequency (OxTOF). CuO CuFe2O4 exhibit largest density possess highest oxidizing ability series, as estimated by reaction, followed CeO2 NM-211 then titania (DT-51 NM-101) Fe3O4. depletion ZnO NM-110 associated with dissolved zinc ions particles; however, basic characteristics particles evidenced TPSR. assays allow ranking eight into three categories statistically different potentials: are most reactive; ceria exhibits moderate reactivity; iron oxide titanias low potential.

Language: Английский

Designing nanotheranostics with machine learning DOI
Lang Rao, Yuan Yuan, Xi Shen

et al.

Nature Nanotechnology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 3, 2024

Language: Английский

Citations

34

Machine learning applications in nanomaterials: Recent advances and future perspectives DOI
Liang Yang, Hong Wang,

Deying Leng

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: unknown, P. 156687 - 156687

Published: Oct. 1, 2024

Language: Английский

Citations

28

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures DOI Creative Commons
Vera Kuznetsova, Áine Coogan,

Dmitry Botov

et al.

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(18)

Published: Jan. 19, 2024

Abstract Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design discovery, reducing need for time‐consuming labor‐intensive experiments simulations. In contrast to their achiral counterparts, application machine chiral nanomaterials is still its infancy, with a limited number publications date. This despite great advance development new sustainable high values optical activity, circularly polarized luminescence, enantioselectivity, as well analysis structural chirality by electron microscopy. this review, an methods used studying provided, subsequently offering guidance on adapting extending work nanomaterials. An overview within framework synthesis–structure–property–application relationships presented insights how leverage study these highly complex are provided. Some key recent reviewed discussed Finally, review captures achievements, ongoing challenges, prospective outlook very important field.

Language: Английский

Citations

25

Strategies of Artificial intelligence tools in the domain of nanomedicine DOI

Mohammad Habeeb,

Huay Woon You, Mutheeswaran Umapathi

et al.

Journal of Drug Delivery Science and Technology, Journal Year: 2023, Volume and Issue: 91, P. 105157 - 105157

Published: Nov. 10, 2023

Language: Английский

Citations

31

Predicting zeta potential of liposomes from their structure: A nano-QSPR model for DOPE, DC-Chol, DOTAP, and EPC formulations DOI Creative Commons
Kamila Jarzyńska, Agnieszka Gajewicz, Krzesimir Ciura

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2024, Volume and Issue: 25, P. 3 - 8

Published: Jan. 24, 2024

Language: Английский

Citations

10

Does the surface charge of the nanoparticles drive nanoparticle–cell membrane interactions? DOI
Sandor Balog, Mauro Sousa de Almeida, Patricia Taladriz‐Blanco

et al.

Current Opinion in Biotechnology, Journal Year: 2024, Volume and Issue: 87, P. 103128 - 103128

Published: April 5, 2024

Language: Английский

Citations

10

Experimentally validated screening strategy for alloys as anode in Mg-air battery with multi-target machine learning predictions DOI

Ning Ling,

Yingying Wang, Shanshan Song

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 496, P. 153824 - 153824

Published: July 6, 2024

Language: Английский

Citations

10

Machine learning-assisted carbon dots synthesis and analysis: state of the art and future directions DOI
Fanyong Yan, Ruixue Bai, Juanru Huang

et al.

TrAC Trends in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 118141 - 118141

Published: Jan. 1, 2025

Language: Английский

Citations

2

The integration of nanotechnology, nanomedicine, and artificial intelligence for advancements in healthcare: a Conceptual Review Based on PRISMA Method and Future Research Directions DOI
Piumika Yapa, Sisitha Rajapaksha, Imalka Munaweera

et al.

Next research., Journal Year: 2025, Volume and Issue: unknown, P. 100330 - 100330

Published: April 1, 2025

Language: Английский

Citations

1

Recent Development of Nanomaterials for Chemical Engineering DOI Creative Commons
Meiwen Cao

Nanomaterials, Journal Year: 2024, Volume and Issue: 14(5), P. 456 - 456

Published: March 1, 2024

There has been an explosive growth in research on nanomaterials since the late 1980s and early 1990s [...]

Language: Английский

Citations

6