Environmentally-Friendly Designs and Syntheses of Metal-Organic Frameworks (MOFs)
Bibliographic record
Abstract
Over the past 15 years, the area of metal-organic frameworks (MOFs) has been the subject of intense efforts in research and development, both on the laboratory and on the industrial scale. Such persistent interest, fueled largely by the desire to develop new functional materials for storage of fuel gases (e.g. hydrocarbons, hydrogen) has led to MOFs being recognized as enabling materials for a number of technologies. Such rising importance of MOFs, accompanied by their recent commercialization, has begun to highlight previously unknown challenges associated with safe, environmentally-friendly synthesis of metal-organic materials on a large scale. These challenges have recently been summarized in a set of evaluative criteria for industrial MOF synthesis, which kindle the need to develop clean and sustainable methods of their manufacture. However, in contrast to organic synthesis, concepts of green and sustainable chemistry have been slow to adopt in inorganic synthesis. This lack of suitable "green" and industrially acceptable synthetic concepts can now be recognized as an outstanding challenge in the MOF area. Focusing on MOF synthesis, we now highlight three recent experimental developments which advance sustainable and environmentally-friendly synthesis with respect to the reaction environment, reactant choice and the synthetic design: (1) using (near) supercritical water as the reaction medium, (2) using biocompatible building blocks and (3) direct conversion of metal oxides into MOFs. These developments signal an important and necessary shift of green inorganic chemistry from designing "green" materials to conducting syntheses mindful of sustainability and environmental impact. In the context of recently established criteria for MOF manufacture these developments also serve to illustrate how embracing aspects of green chemistry and sustainability can be compatible with the requirements of industrial production.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".