Arsenic removal from water and soils using pristine and modified biochars
Bibliographic record
Abstract
Abstract Arsenic (As) is recognized as a persistent and toxic contaminant in the environment that is harmful to humans. Biochar, a porous carbonaceous material with tunable functionality, has been used widely as an adsorbent for remediating As-contaminated water and soils. Several types of pristine and modified biochar are available, and significant efforts have been made toward modifying the surface of biochars to increase their adsorption capacity for As. Adsorption capacity is influenced by multiple factors, including biomass pyrolysis temperature, pH, the presence of dissolved organic carbon, surface charge, and the presence of phosphate, silicate, sulfate, and microbial activity. Improved As adsorption in modified biochars is attributed to several mechanisms including surface complexation/precipitation, ion exchange, oxidation, reduction, electrostatic interactions, and surface functional groups that have a relatively higher affinity for As. Modified biochars show promise for As adsorption; however, further research is required to improve the performance of these materials. For example, modified biochars must be eco-friendly, cost-effective, reliable, efficient, and sustainable to ensure their widespread application for immobilizing As in contaminated water and soils. Conducting relevant research to address these issues relies on a thorough understanding of biochar modifications to date. This study presents an in-depth review of pristine and modified biochars, including their production, physicochemical properties, and As adsorption mechanisms. Furthermore, a comprehensive evaluation of biochar applications is provided in As-contaminated environments as a guide for selecting suitable biochars for As removal in the field. Graphical Abstract
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".