Bio-inspired synthetic pathways and beyond: integrative chemistry
Bibliographic record
Abstract
Herein are described some rational synthetic pathways for generating complex architectures with enhanced application in either optics, catalysis, phase separation or magnetism. The ability of integrative chemistry to scissor condensed matter at several length scales where final objects will be macroscopically one-dimensional (1D), two-dimensional (2D) or three-dimensional (3D) is discussed. In this general context, the first section deals with fibers generated either through electrospinning or extrusion processes bearing, respectively, magnetic and sensor properties. The second part is dedicated to periodic mesostructured thin films (POMTFs) and nanotextured films obtained, respectively viaEISA and Langmuir–Blodgett techniques, where optical properties will be an issue in both cases through respectively sensing and photo band gap properties. Finally the third part will dedicated to pseudo 3D objects, namely membranes, and 3D mesomacrocellular foams, promoted respectively by mesoscale-driven self organization and emulsion-based synthetic routes where final applications will range from filtration to heterogeneous catalysis. After briefly discussing some challenges that should be addressed in the future for “integrative chemistry”, we conclude that it should be seen as an “interdisciplinary tool box”, being a specific space of freedom where each chemist can express his or her own creativity through a rational approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".