Iron–Carbonyl Aqueous Vesicles (MCsomes) by Hydration of [Fe(CO){CO(CH<sub>2</sub>)<sub>5</sub>CH<sub>3</sub>}(Cp)(PPh<sub>3</sub>)] (FpC6): Highly Integrated Colloids with Aggregation‐Induced Self‐Enhanced IR Absorption (AI‐SEIRA)
Bibliographic record
Abstract
Self-assembly of hydrophobic molecules into aqueous colloids contradicts common chemical intuition, but has been achieved through hydration of [Fe(CO){CO(CH2)5CH3}(Cp)(PPh3)] (FpC6). FpC6 has no surface activity, no NMR signals in D2O and no critical aggregation concentration (CAC) in H2O. The molecule, however, contains both acyl and terminal CO groups that are prone to being hydrated. By adding water to a solution in THF, self-assembly of FpC6 can be initiated through water-carbonyl interactions (WCIs) with the highly polarized acyl CO groups. This aggregation subsequently enhances the hydration of the acyl CO groups and also induces the WCI of otherwise unhydrated terminal CO groups. The resultant metal-carbonyl aggregates have been proved to be bilayer vesicles with iron complexes exposed towards water and alkyl chains forming inner walls (MCsomes). These MCsomes show high structure integration upon dilution due to the hydrophobic nature of the building blocks. The highly polarized CO groups on the surface of the MCsomes result in a negative zeta potential (-65 mV) and create a local electric field, which significantly enhances the IR absorption of CO groups by more than 100-fold. This is the first discovery of aggregation-induced self-enhanced IR absorption (AI-SRIRA) without the assistant of external dielectric substrates. Highly integrated MCsomes are, therefore, promising as a novel group of materials, for example, for IR-based sensing and imaging.
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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) | 0.000 | 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 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".