Performance of delithiated ion-sieve material in lithium adsorption and desorption: Direct addition and membrane immobilization approaches
Bibliographic record
Abstract
This study aimed to advance the recovery of lithium ions (Li+) from salt solutions using hydrogenated titanium oxide (HTO), obtained through the delithiation of lithium titanium oxide (LTO). After delithiation, 88.0 ± 1.2% of Li content was extracted from LTO, increasing surface area from 2.1–2.8 m2/g to 5.56–7.52 m2/g and reducing particle size from 296.2 ± 43.14 nm to 95.15 ± 17.23 nm. HTO’s adsorption capacity was examined under three conditions: 1) dispersed HTO in simple Li+ solution (750 mg/L) to determine maximum adsorption capacity 2) dispersed HTO in salt solution where Li+ was present at a realistic concentration (0.71 mg/L) alongside competing ions (Na+ and Mg2+); 3) with HTO present in a membrane coating, to evaluate how immobilization of the material affects the adsorption capacity. Results showed that HTO (50 g/L) achieved a maximum adsorption capacity of 7.2 ± 0.1 mg/g and a desorption of 6.8 ± 0.2 mg/g from simple Li+ solution. In salt solutions, HTO exhibited an adsorption of 0.017 ± 0.0002 mg/g for the Na++Li+ mixture and 0.015 ± 0.0004 mg/g for Na++Mg2++Li+ mixture. The HTO-coated membrane showed an adsorption capacity of 2.0 ± 0.4 mg/g and desorption of 1.1 ± 0.01 mg/g. These results provide important insights into HTO’s adsorption capacity in both free and immobilized conditions in various Li+ solutions.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".