Biobased Activated Carbon and Its Application
Bibliographic record
Abstract
The increasing environmental concerns regarding the depletion of fossil resources and the excessive production of waste have shifted attention toward sustainable materials derived from renewable resources. Biobased activated carbon (BAC), derived from biomass, has emerged as a promising alternative to conventional fossil-derived activated carbon (AC), offering numerous advantages in terms of sustainability, cost-effectiveness, environmental impact, and wide-ranging applications. The production process of BAC involves the carbonization of biomass materials followed by activation, which enhances its porosity and surface area. These characteristics make BAC highly effective for applications in water and air purification, energy storage, and environmental remediation. In water treatment, BAC is used to remove pollutants like heavy metals, organic contaminants, and microplastics through adsorption. In air purification, it helps eliminate harmful gases and volatile organic compounds (VOCs). Additionally, BAC has emerged as a key material in energy storage technologies, particularly in supercapacitors, due to its high surface area and electrical conductivity. Its use in soil amendment and environmental remediation is also gaining attention for removing toxic substances from contaminated environments. The development of BAC is aligned with global efforts to reduce carbon footprints and promote circular economies. Its versatility and sustainability make BAC a promising material in addressing environmental challenges while providing an alternative to fossil fuel-derived products. This chapter will cover all the possible applications where BAC is being used.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".