High‐temperature CO<sub>2</sub> adsorption over Li<sub>4</sub>SiO<sub>4</sub> sorbents derived from different lithium sources
Bibliographic record
Abstract
Abstract Li 4 SiO 4 is a promising sorbent for high temperature CO 2 capture. It could be synthesized from three different Li sources (LiNO 3 , LiOH, and Li 2 CO 3 ) by using the solid state reaction method. The effects of Li sources on the structure and CO 2 adsorption/desorption properties of Li 4 SiO 4 sorbents were analyzed in this work. The results showed that Li 4 SiO 4 sorbents could be synthesized at a lower temperature by using LiNO 3 and LiOH as the starting materials, which could reduce the sintering during the synthesis process and increase the surface area of synthesized Li 4 SiO 4 . During the CO 2 adsorption/desorption cycles, Li 4 SiO 4 sorbents derived from LiNO 3 and LiOH presented higher initial CO 2 adsorption capacities than those from Li 2 CO 3 . After 15 cycles, the adsorption efficiency of Li 4 SiO 4 derived from LiNO 3 showed no or slight decrease, while that from LiOH rapidly decreased to 20% of the initial value. This was because Li 4 SiO 4 derived from LiNO 3 had high surface area and porosity before CO 2 adsorption/desorption cycles, and its surface area even increased after cycles. However, the surface area of Li 4 SiO 4 derived from LiOH decreased greatly due to serious sintering. For Li 4 SiO 4 derived from Li 2 CO 3 , its morphology and surface area were almost unchanged before and after CO 2 adsorption/desorption cycles.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".