Novel Zr-Doped Thiostannate Spinning Fiber (Fiber-KZrTS) for Highly Efficient and Renewable Recovery of Cesium and Strontium from Geothermal Water
Bibliographic record
Abstract
The efficient and renewable recovery of cesium and strontium by absorption from a new type of geothermal water liquid mineral resource is highly desirable but still challenging. In this work, a new Zr-doped layered potassium thiostannate adsorbent (KZrTS) was first synthesized and used for Cs + and Sr 2+ green and efficient adsorption. It was found that KZrTS had very fast adsorption kinetics toward both Cs + and Sr 2+ with an equilibrium reached within 1 min, and the theoretical maximum adsorption capacities for Cs + and Sr 2+ were 402.84 and 84.88 mg/g, respectively. Moreover, to solve the loss problem of the engineering application of the powdered adsorbent KZrTS, KZrTS was uniformly coated with polysulfone by wet spinning technology to form micrometer-level filament-like absorbents (Fiber-KZrTS), whose adsorption equilibrium rates and capacities toward Cs + and Sr 2+ are almost the same as that of powder. Furthermore, Fiber-KZrTS showed excellent reusability, and the adsorption performance remained virtually unchanged after 20 cycles. Therefore, Fiber-KZrTS has potential application for green and efficient cesium and strontium recovery from geothermal water.
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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".