Exhausted Cr(VI) Sensing/Removal Aerogels Are Recycled for Water Purification and Solar‐Thermal Energy Generation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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‐NH 2 ) 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‐NH 2 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 (MS x ‐CPC gel) and used for solar thermoelectric generation resulting in power generation. Additionally, the MS x ‐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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 it