Understanding and Controlling Polymer–Organometallic Precursor Interactions in Sequential Infiltration Synthesis
Bibliographic record
Abstract
Abstract Sequential infiltration synthesis (SIS) is an emerging method for vapor-phase growth of inorganic materials within polymers that is utilized for hybrid organic–inorganic and inorganic nanostructure fabrication. The range of SIS applications has been continuously expanding for the past decade. A fundamental understanding of precursor–polymer interactions is, however, essential to expand the use of SIS to additional chemistries and move beyond thin film polymer templates. This work utilizes density functional theory (DFT) calculations and in situ gravimetric analysis to probe the growth mechanism of trimethylaluminum (TMA) within poly(methyl methacrylate) (PMMA) and poly(2-vinylpyridine) (P2VP). The theoretical and experimental analyses reveal that each precursor–polymer pair is characterized by a balance point temperature at which rates of forward and reverse precursor–polymer binding enable maximum mass gain at thermodynamic equilibrium. At short exposure times, mass gain is significantly influenced by the pressure profile of the process chamber. Mechanism comprehension enabled nanopatterning of a previously unsuitable block copolymer (BCP), polystyrene-block-P2VP (PS-b-P2VP), at elevated temperatures. It was proven possible to grow significant mass while maintaining the pattern by stabilizing the morphology via a single cycle at low-temperature SIS, thus overcoming self-assembly sensitivity to temperature.
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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) | 0.001 | 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".