Important Counteranion Effect on Adsorption Efficacity of Hydrogen Sulfide by Silver(I)-Dithione Coordination Polymers
Bibliographic record
Abstract
Four new coordination polymers (CPs) have been prepared and evaluated for their efficacy in adsorbing hydrogen sulfide. The reactions of the structurally flexible assembling dithione ligand, L, with different silver(I) salts lead to four new metal–organic coordination architectures (CPs I, III, V, and VIII ) exhibiting either one- or two-dimensional networks. CP I, 2D-[(Ag 2 Cl 2 ) L ] n, exhibits a linear series of rhomboid (S) 2 Ag(μ 2 -Cl) 2 Ag(S) 2 secondary building units (SBUs) where S is one of the thione functions of L, altogether forming a 2D-network. CP III, 2D-[(AgI) L ] n, is built upon parallel staircase-shaped 1D-[Ag 2 (μ 3 -I) 2 ] n SBUs bridged by S atoms of L that form a 2D-grid. CP V, 2D-[(Ag L )(NO 3 )] n, presents parallel 1D-folded S-shaped [Ag L ] n + chains linked by strong argentophilic Ag···Ag interactions, forming a 2D-scaffolding. CP VIII, 1D-[(Ag 2 L 3 )(Cr 2 O 7 )] n, shows 1D-zigzag [{Ag(η 2 -μ 2,η-μ,μ- L )} 2 ] n 2 n + chains accompanied by Cr 2 O 7 2– counteranions. The adsorption isotherms of H 2 S gas by these new CPs were examined and compared to those of related CPs [(Ag 2 Br 2 ) L ] n ( II ), [(AgCN) 4 L ] n ( IV ), [(Ag 2 L )(CF 3 SO 3 ) 2 ] n ( VI ), and [(Ag 2 L )(NO 3 )(ClO 4 )] n ( VII ). Among the tested polymers, the 3D-CP IV featuring cyanide anions exhibits the highest adsorption capacity of the CPs studied in this work. In order to determine the reason for this marked difference, density functional theory (DFT) computations were used. All in all, it turns out that the electrostatic interactions (CN – ···H-SH) are significantly stronger than the O – ···H-SH ones. This investigation provides a valuable conceptual tool for other designs of CPs and MOFs having the purpose of capturing toxic gases.
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.
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".