Optimizing Solvent Conditions for Reduced-Time Solvothermal Synthesis of Ti-MIL-125
Bibliographic record
Abstract
Titanium(IV)-based metal-organic frameworks (MOFs), particularly the highly porous and photo-catalytically active Ti-MIL-125 structure, exhibit significant potential for diverse applications.Conventional solvothermal methodologies often require prolonged synthesis times resulting with a wider particle size range, which poses a limitation for implementation in two-phase microfluidic systems allowing for production of monodispersed smaller particles.This study investigates the influence of different solvent systems on the nucleation and crystallization kinetics of Ti-MIL-125 to achieve reduced synthesis times.While n-methyl-2-pyrrolidone (NMP) as a solvent allowed for temperature-dependent crystallization with optimal results at 220 °C, it did not facilitate substantial time reduction.Incorporation of glycerol to elevate the solution boiling point and allow for use of higher synthesis temperatures without the solvent evaporating, resulted in a deceleration of nucleation kinetics, attributed to the augmented viscosity of the synthesis medium.A ternary solvent mixture of NMP, n,n-dimethylformamide (DMF), and methanol significantly accelerated the synthesis, achieving wellcrystalline Ti-MIL-125 at a lower temperature of 162 °C and a reduced residence time of 4 hours.These findings highlight the critical role of solvent selection and its impact on diffusion and precursor assembly, providing valuable insights for optimizing Ti-MIL-125 synthesis, particularly for production in droplet-based microfluidic platforms.
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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".