Biochar Meets Single‐Atom: A Catalyst for Efficient Utilization in Environmental Protection Applications and Energy Conversion
Bibliographic record
Abstract
Single-atom catalysts (SACs), combining the advantages of multiphase and homogeneous catalysis, have been increasingly investigated in various catalytic applications. Carbon-based SACs have attracted much attention due to their large specific surface area, high porosity, particular electronic structure, and excellent stability. As a cheap and readily available carbon material, biochar has begun to be used as an alternative to carbon nanotubes, graphene, and other such expensive carbon matrices to prepare SACs. However, a review of biochar-based SACs for environmental pollutant removal and energy conversion and storage is lacking. This review focuses on strategies for synthesizing biochar-based SACs, such as pre-treatment of organisms with metal salts, insertion of metal elements into biochar, or pyrolysis of metal-rich biomass, which are more simplistic ways of synthesizing SACs. Meanwhile, this paper attempts to 1) demonstrate their applications in environmental remediation based on advanced oxidation technology and energy conversion and storage based on electrocatalysis; 2) reveal the catalytic oxidation mechanism in different catalytic systems; 3) discuss the stability of biochar-based SACs; and 4) present the future developments and challenges regarding biochar-based SACs.
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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".