From Waste to Wealth: Covalent Organic Frameworks for Gold Detection and Recovery from Secondary Sources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gold is a precious element, renowned for its diverse applications in catalysis, biomedicine, and electronics, largely due to its remarkable stability and superior conductivity. However, the escalating global demand and intensive mining activities have prompted a shift toward exploring gold recovery from alternative and secondary sources. Traditional gold recovery techniques, such as hydrometallurgy and cyanidation, are efficient yet notorious for their toxic byproducts, necessitating the pursuit of more sustainable methods. This review explores the potential of covalent organic frameworks (COFs) as cutting-edge materials for gold detection and adsorption. COFs are distinguished by their precise architecture, inherent porosity, and customizable functionalities, rendering them exceptionally suited for the selective capture of gold. First, we present an overview of the fundamental COF gold adsorption mechanisms, including coordination chemistry, hydrogen bonding, electrostatic interactions, and reduction processes. This is followed by examining COF synthesis methods, functionalization techniques, and composite engineering strategies that optimize their stability and adsorption efficiency. The review further highlights recent advancements in the utilization of COFs for gold sensing, recovery from electronic waste, and adsorption at trace concentrations. Finally, we address the current challenges in the application of COFs in this domain and propose future research directions. This comprehensive review serves as an invaluable resource for advancing gold extraction through COF-based materials, ultimately contributing to innovative and sustainable gold recovery practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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