Examining Metal Identity and Proximity Effects on Acetylene Hydrogenation with Azolate-Based MOFs
Bibliographic record
Abstract
Liquid organic hydrogen carriers (LOHCs) are an attractive fuel source due to their compatibility with existing transportation methods and ease of use. However, they suffer from sluggish (de)hydrogenation kinetics. One promising platform for developing next-generation catalysts is metal–organic frameworks (MOFs), which can enable systematic interrogation into the influence of metal identity and spatial arrangement. In this study, the effect of the coordination environment was investigated using Ni- and Co-based azolate MOFs: MFU-4 l -OH (M x Zn 5– x (OH) 4 (BTDD) 3; x = 4 for M = Co and x = 3 for M = Ni, H 2 BTDD = bis(1H-1,2,3-triazolo[4,5- b ][4′,5′-i])dibenzo[1,4]dioxin), composed of single-site nodes, and M(OH) 2 BBTA (M = Ni, Co; H 2 BBTA = 1H,5H-benzo(1,2-d:4,5-d’)bistriazole), composed of extended chain-type nodes. The catalysts were characterized by isotherms, powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), inductively coupled plasma-optical emission spectroscopy (ICP-OES), and X-ray photoelectron spectroscopy (XPS) analysis. Acetylene hydrogenation activity under steady state conditions (150 °C, 1:1 C 2 H 2:H 2 ) revealed higher turnover frequencies (TOFs) up 1.8 × 10 –4 min –1 and 3.0 × 10 –5 min –1 for Ni-MFU-4 l -OH and Co-MFU-4 l -OH, respectively, compared to their BBTA analogues. However, the Co-based MOFs, particularly Co 2 (OH) 2 -BBTA, exhibited greater selectivity (up to 19%) for the fully hydrogenated ethane product. Isosteric heat of adsorption ( Q st ) measurements for ethylene and ethane revealed that the BBTA framework had stronger binding to the products than MFU-4 l . These findings demonstrate that metal identity and coordination environment may modulate acetylene hydrogenation performance, leading to design principles for tuning LOHC hydrogenation catalysts.
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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".