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

и другие.

Environmental Science Nano, Год журнала: 2024, Номер 11(9), С. 3744 - 3760

Опубликована: Янв. 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.

Язык: Английский

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

и другие.

Nature Nanotechnology, Год журнала: 2024, Номер unknown

Опубликована: Окт. 3, 2024

Язык: Английский

Процитировано

34

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

Deying Leng

и другие.

Chemical Engineering Journal, Год журнала: 2024, Номер unknown, С. 156687 - 156687

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

28

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

Dmitry Botov

и другие.

Advanced Materials, Год журнала: 2024, Номер 36(18)

Опубликована: Янв. 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.

Язык: Английский

Процитировано

25

Strategies of Artificial intelligence tools in the domain of nanomedicine DOI

Mohammad Habeeb,

Huay Woon You, Mutheeswaran Umapathi

и другие.

Journal of Drug Delivery Science and Technology, Год журнала: 2023, Номер 91, С. 105157 - 105157

Опубликована: Ноя. 10, 2023

Язык: Английский

Процитировано

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

и другие.

Computational and Structural Biotechnology Journal, Год журнала: 2024, Номер 25, С. 3 - 8

Опубликована: Янв. 24, 2024

Язык: Английский

Процитировано

10

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

и другие.

Current Opinion in Biotechnology, Год журнала: 2024, Номер 87, С. 103128 - 103128

Опубликована: Апрель 5, 2024

Язык: Английский

Процитировано

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

и другие.

Chemical Engineering Journal, Год журнала: 2024, Номер 496, С. 153824 - 153824

Опубликована: Июль 6, 2024

Язык: Английский

Процитировано

10

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

и другие.

TrAC Trends in Analytical Chemistry, Год журнала: 2025, Номер unknown, С. 118141 - 118141

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

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

и другие.

Next research., Год журнала: 2025, Номер unknown, С. 100330 - 100330

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

1

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

Nanomaterials, Год журнала: 2024, Номер 14(5), С. 456 - 456

Опубликована: Март 1, 2024

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

Язык: Английский

Процитировано

6