Effective and selective gold recovery based on synergistic bimetallic-hydroxyl metal–organic frameworks
Why this work is in the frame
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Bibliographic record
Abstract
• A bimetallic-hydroxyl synergistic strategy enables efficient gold recovery from wastewater. • The adsorbents exhibit superior Au(III) extraction performance over a wide pH range. • The self-desorption of high-purity gold (∼23.9 K) without post-treatment is achieved. • Adsorption, nucleation and desorption mechanisms of gold species are unraveled. • The adsorbents demonstrate the highly selective extraction of gold from complex water matrices. Efficient recovery of high-value metals such as gold from wastewater is critical for environmental remediation and sustainable resource recovery, yet it poses considerable challenges. Herein, we propose a novel design strategy that utilizes the synergistic effects of bimetallic sites and hydrophilic functionalities within a metal–organic framework for efficient gold recovery. A mechanism whereby Au(III) is directly reduced to Au(0), bypassing the formation of Au(I) intermediates, is facilitated by the bimetallic sites, as evidenced by spectroscopic analyses and theoretical simulations. Concurrently, the hydrophilic hydroxyl groups facilitate the nucleation and detachment of high-purity gold nanoparticles (∼23.9 K) without post-treatment. The Fe 1 Co 1 - MOF - 74 synthesized using this strategy demonstrates superior performance in Au(III) adsorption, achieving a remarkable capacity of ∼ 3078.00 mg g −1 , high selectivity (distribution coefficient of 1.2 × 10 7 mL g −1 ), and a broad pH applicability (1.0–9.0), outperforming previously reported MOFs. Furthermore, the practical application of Fe 1 Co 1 -MOF-74 is demonstrated by the highly selective extraction of gold from complex water matrices, including river, lake, simulated seawater and central processing unit (CPU) leachate. This work offers promising strategies for sustainable gold reclamation from complex aqueous environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it