Dual‐Ligand Metal‐Organic Frameworks via In Situ Amidoxime Engineering for Selective Ion Separation
Bibliographic record
Abstract
Abstract Metal‐organic frameworks (MOFs) are attractive candidates for ion extraction due to their ordered porous architectures. However, their inert surfaces with few binding sites limit their use in selective ion separation. Herein, inspired by the dual‐ligand coordination of oxime groups in biological carriers, a precise in situ amidoxime surface functionalization strategy (MOFs‐AO) is reported that preserves MOFs architecture while enabling high‐density controlled functionalization of amidoxime moieties for selective Ga(III) extraction. Density functional theory (DFT) calculations and molecular force measurements reveal that, unlike conventional monodentate with low electron density, amidoxime groups provide reinforced interactions with Ga(III) through spatially optimized N,O dual‐ligand coordination configuration. As a result, MOFs‐AO achieve a Ga(III) capacity of 205.13 mg g −1 and a Ga(III)/V(V) ratio exceeding 7.0 in challenging Bayer liquor, nearly one order of magnitude higher than comparable materials. When integrated into a polymeric network, the MOFs‐AO form a flow‐through reactor exhibiting high water flux (>1250 L m −2 h −1 ) and continuous Ga(III) recovery efficiency above 90% through successive in situ adsorption–desorption cycles. This work demonstrates a robust and generalizable surface‐engineering strategy for MOFs functionalization, advancing sustainable and selective ion separation.
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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".