New horizons in synthesis, functionalization, and deposition of advanced materials using multifunctional organic alkalizers
Bibliographic record
Abstract
This review describes new strategies in the use of multifunctional organic alkalizers (OA) for the fabrication of advanced functional materials. OA facilitate solubilization and delivery of poorly solubilized drugs through the formation of drug-OA complexes and supramolecular gels. OA are applied for the synthesis of materials for biomedical, energy storage, catalytic, photovoltaic, sensor, and electronic applications. The synthesis of nanocrystals with controlled size, crystal phase, shape, and tunable facets in the presence of OA-capping agents is described. The synthesis of materials for energy storage in batteries and supercapacitors using OA or adding OA to electrolytes results in improved power-energy characteristics and cyclic stability. The multifunctional OA allow the fabrication of advanced bioceramics and biocements, novel supramolecular gels, surface modification of materials for advanced catalytic, water purification, and sensor applications. Various challenges in the dispersion of functional nanoparticles are addressed for colloidal manufacturing. The ability of the OA to form supramolecular gels paves the way for the development of novel functional materials. New strategies in the solubilization of polymers and other functional materials open an avenue for the development of advanced electrodeposition methods for the deposition of polymer and composite films. Fundamental mechanisms and future research avenues are described.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".