Sustainable carbon materials
Bibliographic record
Abstract
Carbon-based structures are the most versatile materials used in the modern field of renewable energy (i.e., in both generation and storage) and environmental science (e.g., purification/remediation). However, there is a need and indeed a desire to develop increasingly more sustainable variants of classical carbon materials (e.g., activated carbons, carbon nanotubes, carbon aerogels, etc.), particularly when the whole life cycle is considered (i.e., from precursor "cradle" to "green" manufacturing and the product end-of-life "grave"). In this regard, and perhaps mimicking in some respects the natural carbon cycles/production, utilization of natural, abundant and more renewable precursors, coupled with simpler, lower energy synthetic processes which can contribute in part to the reduction in greenhouse gas emissions or the use of toxic elements, can be considered as crucial parameters in the development of sustainable materials manufacturing. Therefore, the synthesis and application of sustainable carbon materials are receiving increasing levels of interest, particularly as application benefits in the context of future energy/chemical industry are becoming recognized. This review will introduce to the reader the most recent and important progress regarding the production of sustainable carbon materials, whilst also highlighting their application in important environmental and energy related fields.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".