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Peer-reviewedCC BYResults in Chemistry

Transformative role of nanomaterials: Classification, physicochemical behavior, synthesis, characterization, and technological applications

  • Birhanu Abera1,2,3
  • Gebregziabher Kahsay3
  • Tamiru Negussie3
  • Gedefaw Mebratie3
  1. 1College of Natural and Computational Sciences, Department of Physics, Injibara University, Injibara, Ethiopia
  2. 2College of Natural and Computational Sciences, Department of Physics, Mekdela Amba University, Gimba, Ethiopia
  3. 3College of Science, Department of Physics, Bahir Dar University, Bahir Dar, Ethiopia
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Abstract

This review presents a coherent analytical framework that links technical performance to physicochemical behavior, synthesizing methods, and nanomaterial classification. Unlike conventional descriptive assessments, this work critically assesses how dimensionality (0D 3D), composition, and morphology control size-dependent optical, electrical, and mechanical properties through transport mechanisms, surface energy, and quantum confinement. The constant scaling gap in nanofabrication is highlighted by quantitative comparisons between bottom-up and bottom-up synthesis methods in terms of scale, cost, reproducibility, and environmental effect. The Brus equation and the local surface plasma resonance (LSPR), two important control models, are analyzed, and their physical limitations and areas of application are explicitly discussed. To show how complicated nanoscale phenomena can be resolved, integrated multitechnological classification methods are studied. To identify important barriers to industrial translation, the review also assesses specific performance measures in the areas of health care, energy, and nanoelectronics. Finally, we offer a forward-looking roadmap for next-generation nanotechnologies by discussing future directions, including sustainable green synthesis and the design of materials assisted by machine learning.

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Cite

BibTeXRIS
BibTeX
@article{abera2026transformative,
  title = {{Transformative role of nanomaterials: Classification, physicochemical behavior, synthesis, characterization, and technological applications}},
  author = {Birhanu Abera and Gebregziabher Kahsay and Tamiru Negussie and Gedefaw Mebratie},
  journal = {Results in Chemistry},
  year = {2026},
  month = may,
  doi = {10.1016/j.rechem.2026.103450},
  url = {https://doi.org/10.1016/j.rechem.2026.103450},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Transformative role of nanomaterials: Classification, physicochemical behavior, synthesis, characterization, and technological applications
AU  - Birhanu Abera
AU  - Gebregziabher Kahsay
AU  - Tamiru Negussie
AU  - Gedefaw Mebratie
PY  - 2026
DA  - 2026/05/19
JO  - Results in Chemistry
DO  - 10.1016/j.rechem.2026.103450
UR  - https://doi.org/10.1016/j.rechem.2026.103450
AB  - This review presents a coherent analytical framework that links technical performance to physicochemical behavior, synthesizing methods, and nanomaterial classification. Unlike conventional descriptive assessments, this work critically assesses how dimensionality (0D 3D), composition, and morphology control size-dependent optical, electrical, and mechanical properties through transport mechanisms, surface energy, and quantum confinement. The constant scaling gap in nanofabrication is highlighted by quantitative comparisons between bottom-up and bottom-up synthesis methods in terms of scale, cost, reproducibility, and environmental effect. The Brus equation and the local surface plasma resonance (LSPR), two important control models, are analyzed, and their physical limitations and areas of application are explicitly discussed. To show how complicated nanoscale phenomena can be resolved, integrated multitechnological classification methods are studied. To identify important barriers to industrial translation, the review also assesses specific performance measures in the areas of health care, energy, and nanoelectronics. Finally, we offer a forward-looking roadmap for next-generation nanotechnologies by discussing future directions, including sustainable green synthesis and the design of materials assisted by machine learning.
SN  - 2211-7156
ER  -