Bibliographic record
Abstract
Abstract The incorporation of hydrogen‐bond donor or acceptor groups into ligands followed by their coordination to inert transition metals has proven to be a successful methodology for creating unique metal complex receptors for a wide variety of anions, some neutral molecules, and a limited number of cations. The metal ion can participate in binding a guest indirectly via second‐sphere coordination or directly via simultaneous first‐ and second‐sphere coordination. This chapter contains two major sections that outline the design strategies and describe a variety of metal complexes that function as receptors for (i) neutral molecules, (ii) cations, and (iii) anions. Under the heading of receptors for neutral molecules, the focus is on increasing complexity with respect to the targeted guest beginning with simple species such as H 2 O and NH 3 and featuring biologically important molecules such as DNA nucleobases. The section on metal complexes as anion receptors is structured to shed light on the different avenues used to design receptors and the structural features of these systems. A common theme is the idea that host–guest assemblies may have complicated structures, but the ligands themselves do not require tedious synthetic routes to prepare and coordination to the metal center is facile. Thus, the combination of coordination chemistry and noncovalent interactions such as hydrogen bonding and π‐stacking can be viewed as a powerful tool for molecular and ion recognition. The state of the art is advanced enough to allow targeting of specific guests by tailoring both the metal–ligand and host–guest interactions.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".