Recent Advances in Bio‐Templated Metallic Nanomaterial Synthesis and Electrocatalytic Applications
Bibliographic record
Abstract
Developing metallic nanocatalysts with high reaction activity, selectivity and practical durability is a promising and active subfield in electrocatalysis. In the classical "bottom-up" approach to synthesize stable nanomaterials by chemical reduction, stabilizing additives such as polymers or organic surfactants must be present to cap the nanoparticle to prevent material bulk aggregation. In recent years, biological systems have emerged as green alternatives to support the uncoated inorganic components. One key advantage of biological templates is their inherent ability to produce nanostructures with controllable composition, facet, size and morphology under ecologically friendly synthetic conditions, which are difficult to achieve with traditional inorganic synthesis. In addition, through genetic engineering or bioconjugation, bio-templates can provide numerous possibilities for surface functionalization to incorporate specific binding sites for the target metals. Therefore, in bio-templated systems, the electrocatalytic performance of the formed nanocatalyst can be tuned by precisely controlling the material surface chemistry. With controlled improvements in size, morphology, facet exposure, surface area and electron conductivity, bio-inspired nanomaterials often exhibit enhanced catalytic activity towards electrode reactions. In this Review, recent research developments are presented in bio-approaches for metallic nanomaterial synthesis and their applications in electrocatalysis for sustainable energy storage and conversion systems.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".