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 Li4SiO4 is a promising sorbent for high temperature CO2 capture. It could be synthesized from three different Li sources (LiNO3, LiOH, and Li2CO3) by using the solid state reaction method. The effects of Li sources on the structure and CO2 adsorption/desorption properties of Li4SiO4 sorbents were analyzed in this work. The results showed that Li4SiO4 sorbents could be synthesized at a lower temperature by using LiNO3 and LiOH as the starting materials, which could reduce the sintering during the synthesis process and increase the surface area of synthesized Li4SiO4. During the CO2 adsorption/desorption cycles, Li4SiO4 sorbents derived from LiNO3 and LiOH presented higher initial CO2 adsorption capacities than those from Li2CO3. After 15 cycles, the adsorption efficiency of Li4SiO4 derived from LiNO3 showed no or slight decrease, while that from LiOH rapidly decreased to 20% of the initial value. This was because Li4SiO4 derived from LiNO3 had high surface area and porosity before CO2 adsorption/desorption cycles, and its surface area even increased after cycles. However, the surface area of Li4SiO4 derived from LiOH decreased greatly due to serious sintering. For Li4SiO4 derived from Li2CO3, its morphology and surface area were almost unchanged before and after CO2 adsorption/desorption cycles.
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.
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 | 0.000 | 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".