Protein Assisted Fabrication of Metallic Nanorings and Spherical Nanoparticles for Electrocatalytic and Organocatalytic Applications
Bibliographic record
Abstract
Inspiration from nature has driven the development and application of greener nanomaterials prepared using biotemplates in the field of nanoscience. Compared to traditional chemical routes, bioinspired nanomaterial synthesis has the advantage of mild synthetic conditions such as aqueous environment, ambient temperature, and no requirement for organic capping agent. In addition, the structures of some biomolecules make it possible to fabricate nanomaterials with complex and interesting morphology that are difficult to achieve through chemical methods. In this study, tobacco mosaic virus coat protein (TMV cp) was investigated as a versatile template to mediate the synthesis of nanorings and spherical nanoparticles under neutral and alkaline conditions, respectively. While the prepared silver nanorings displayed superior selectivity (95% Faradaic Efficiency) and stability of catalyzing CO2 electroreduction (CO2 RR) compared to free silver nanoparticles prepared by chemical methods, the platinum nanorings showed excellent electrocatalytic activity for hydrogen evolution reaction (HER). Spherical nanoparticles synthesized under alkaline conditions (Pt, Pd and Au NPs) were also investigated for their catalytic behavior towards organic transformations such as 4-nitrophenol reduction and alkyne hydrogenation. References: [1] Huang J. et al. Chem. Soc. Rev., 2015, 44, 6330. [2] Pan Y. et al. Nanoscale, 2019, 11, 1895. [3] Kim C. et al. J. Am. Chem. Soc., 2015, 137, 13844. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".