A New Approach to Characterizing Sorption in Materials with Flexible Micropores
Bibliographic record
Abstract
Microporous dipeptides, also known as organic zeolites or biozeolites, as examples of small-pore peptide nanotubes provide a convenient set of materials for developing a systematic approach based on 129 Xe NMR spectroscopy for the derivation of thermodynamic and molecular scale information on temperature dependent pore filling. The sorption of xenon in the isolated 1D chiral nanochannels of eight microporous dipeptides Ala-Val (AV), Val-Ala (VA), Leu-Ser (LS), Ala-Ile (AI), Val-Val (VV), Ile-Ala (IA), Ile-Val (IV), Val-Ile (VI) (all LL isomers) was monitored in situ with continuous-flow 129 Xe NMR spectroscopy over a temperature range of 173−343 K. The materials all showed strongly anisotropic signals, with isotropic chemical shift changing from 95 to 281 ppm depending on the dipeptide used and/or temperature. The isosteric heats of sorption (q st ) and entropy factors were determined from two independent models. The sorption process was complicated by reversible phase transformations of some dipeptides and irreversible changes due to aging of samples, both of which may be of considerable importance in applications of soft materials. The interpretation of the line shapes and chemical shift anisotropy as a function of temperature provided information on the structure of the xenon-cavity complex and made it possible taking into account the helicity and flexibility of the nanochannels and the dynamics of xenon. The approach illustrates a powerful way of analyzing pore space in soft microporous materials, yielding a quantitative thermodynamic description of sorption and the characteristics of the pore space and sorption events that occur on molecular-scale level during pore filling.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".