Exhausted Cr(VI) Sensing/Removal Aerogels Are Recycled for Water Purification and Solar‐Thermal Energy Generation
Bibliographic record
Abstract
Abstract Heavy metal pollution has resulted in numerous environmental challenges. However, classic approaches, involving the use of solid adsorbents are subject to limitations, including the high energy consumption required for processing before and after use. Accordingly, strategies that facilitate the use of metal capture media that extends beyond waste remediation are attractive. Herein, a porous fluorescent aerogel (CPC aerogel) is constructed by immersing amino‐based carbon dots (CDs‐NH2) into a polyethyleneimine (PEI)/carboxymethylated cellulose (CMC) aerogel network for the simultaneous detection and adsorption of Cr(VI). Adsorption experiments confirm that the CMC/PEI containing CDs‐NH2 aerogel (CPC aerogel) exhibits good Cr(VI) extraction capacity, and can reach a level that conforms with industrial water safety standards. In addition, the CPC aerogel can continuously detect and remove Cr(VI) at high flux. Following Cr(VI) absorption, the CPC aerogel may be vulcanized (MSx‐CPC gel) and used for solar thermoelectric generation resulting in power generation. Additionally, the MSx‐CPC gel can be used for solar steam generation and exhibits excellent evaporation rates of ≈1.31 kg m–2 h–1 under one sun irradiation. The results serve to underscore how materials designed for metal ion recognition and adsorption once exhausted can be exploited to provide materials for solar thermoelectric power generation and seawater desalination.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".