Surfactant-Template/Ultrasound-Assisted Method for the Preparation of Porous Nanoparticle Lithium Zirconate
Bibliographic record
Abstract
Porous nanoparticle lithium zirconate (Li 2 ZrO 3 ) was prepared using an ultrasound-assisted surfactant-template method in the liquid-state reaction. The CO 2 adsorption performance of the prepared materials was tested under various conditions and compared with that of Li 2 ZrO 3 prepared by the simple surfactant-template method (porous, without sonication) and the conventional soft-chemistry route. The results indicated a better adsorption rate and capacity of porous nanopowders, whether assisted with ultrasound or not, in comparison with the traditional sample. This behavior is mainly due to a less aggregated powder structure and porous framework, facilitating gas and ion diffusion to and from the particle layers. However, the porous adsorbent prepared without sonication exhibited instability during cyclic operation, limiting its application for long-time use. Sonication time and surfactant concentration were found to be key parameters for controlling the crystallite size and the BET surface area. The porous Li 2 ZrO 3 sample prepared with less surfactant and a shorter irradiation time (sample A) had the most favorable sorption kinetics and capacity among all studied samples. The maximum uptake capacity of 22 wt % for sample A compared to 15.2 wt % for the conventional sample (sample J, fabricated by the soft-chemistry method), obtained under a 100% CO 2 stream, suggested a noticeable improvement in sorption behavior of the proposed adsorbents compared with traditional Li 2 ZrO 3 . Moreover, the adequate cyclic stability of porous powders prepared by sonication identify these materials as promising CO 2 acceptors, particularly for integrated sorbent/catalyst systems such as that used for sorption-enhanced steam methane reforming (SESMR). CO 2 adsorption experimental data for sample A were successfully modeled at various CO 2 partial pressures using a double-exponential equation.
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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".