Removal of Pb(II) from industrial wastewater by using various natural materials – a review
Bibliographic record
Abstract
Lead's history in science, medicine and technology has been overshadowed by its notoriety as a poison in homicides.Lead is viewed as being synonymous with toxicity.Dangerous lead concentration in natural, as well as industrial wastewater, is now a worldwide problem and often referred to as a 20th-21st century calamity.Existing overviews of lead removal include technologies that have traditionally been used (oxidation, precipitation/coagulation/membrane separation) with far less attention paid to adsorption.No previous review is available where readers can get an overview of the sorption capacities of both available and developed sorbents used for lead remediation.Most of the valuable available literature on lead remediation by adsorption has been incorporated.Lead sorption by commercially available carbons and other low-cost adsorbents developed from various natural materials are surveyed and critically reviewed and their sorption effi ciency compared.Some commercially available adsorbents are also surveyed.An extensive table summarizes the maximum percentage of Pb(II) uptake, optimum pH, isothermal model fi t and thermodynamic parameters of various adsorbents.Some low cost adsorbents prepared from various natural materials are superior including bagasse fl y ash, bone powder, sea nodule, olive cake, polymerized banana stem, tea waste, carbons developed from agricultural waste (coconut shell carbon and palm shell carbon), biosorbents (modifi ed lignin and microspore), phosphogypsum and some commercial adsorbents, which included activated carbon, carbon aerogel tested for lead removal come out to be superior.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".