Thermodynamic Insights into Phosphonate Binding in Metal–Azolate Frameworks
Bibliographic record
Abstract
Organophosphorus chemicals, including chemical warfare agents (CWAs) and insecticides, are acutely toxic materials that warrant capture and degradation. Metal–organic frameworks (MOFs) have emerged as a class of tunable, porous, crystalline materials capable of hydrolytically cleaving, and thus detoxifying, several organophosphorus nerve agents and their simulants. One such MOF is M-MFU-4l (M = metal), a bioinspired azolate framework whose metal node is composed of a variety of divalent first-row transition metals. While Cu-MFU-4l and Zn-MFU-4l are shown to rapidly degrade CWA simulants, Ni-MFU-4l and Co-MFU-4l display drastically lower activities. The lack of reactivity was hypothesized to arise from the strong binding of the phosphate product to the node, which deactivates the catalyst by preventing turnover. No such study has provided detailed insight into this mechanism. Here, we leverage isothermal titration calorimetry (ITC) to monitor the binding of an organophosphorus compound with the M-MFU-4l series to construct a complete thermodynamic profile ( K a, Δ H, Δ S, Δ G ) of this interaction. This study further establishes ITC as a viable technique to probe small differences in thermodynamics that result in stark differences in material properties, which may allow for better design of first-row transition metal MOF catalysts for organophosphorus hydrolysis.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".