Bibliographic record
Abstract
Taken to the molecular level, the concept of "tweezers" opens a rich and fascinating field at the convergence of molecular recognition, biomimetic chemistry and nanomachines. Composed of a spacer bridging two interaction sites, the behaviour of molecular tweezers is strongly influenced by the flexibility of their spacer. Operating through an "induced-fit" recognition mechanism, flexible molecular tweezers select the conformation(s) most appropriate for substrate binding. Their adaptability allows them to be used in a variety of binding modes and they have found applications in chirality signalling. Rigid spacers, on the contrary, display a limited number of binding states, which lead to selective and strong substrate binding following a "lock and key" model. Exquisite selectivity may be expressed with substrates as varied as C(60) , nanotubes and natural cofactors, and applications to molecular electronics and enzyme inhibition are emerging. At the crossroad between flexible and rigid spacers, stimulus-responsive molecular tweezers controlled by ionic, redox or light triggers belong to the realm of molecular machines, and, applied to molecular tweezing, open doors to the selective binding, transport and release of their cargo. Applications to controlled drug delivery are already appearing. The past 30 years have seen the birth of molecular tweezers; the next many years to come will surely see them blooming in exciting applications.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".