Bibliographic record
Abstract
Abstract The sections in this article are Introduction Ordered Mesoporous Molecular Sieves:MCM‐41 Synthesis of Ordered Mesoporous Materials Synthesis Strategies for Mesostructure Formation Inorganic Polymerization and Self‐Assembly with Surfactants A Inorganic Polymerization (the Case of Silica) B Template‐Assisted Synthesis C Surfactant Packing D Formation of the Mesostructure E True Liquid Crystal Templating F Evaporation‐Induced Self‐Assembly Synthesis Pathways and Structural Diversity A Silica Polymorphs from the Alkaline Route (S+I−) B Silica Polymorphs from the Acidic Route (S+X−I+) C Anionic Surfactant‐Templated Mesoporous Silicas: AMS‐nMaterials D Non‐Ionic Routes (Hydrogen‐Bonding InteractionsS0I0,N0I0or (N0H+) (X−I+) Pore Size Tailoring and Structure Engineering A Surfactant Chain Length B Time and Temperature C Effects of Electrolytes and pH Adjustment D Organic Additives E Stability and Zeolitization Removal of the Template A Calcination B Solvent Extraction and Acid Treatments Functionalization of Ordered Mesoporous Materials Functionalization Strategies Surface Properties Surface Functionalization Framework Functionalization Non‐Siliceous Mesostructured and Mesoporous Materials Transition Metal Oxides Alumina Other Non‐Siliceous Compositions Hard Templating (Nanocasting) Morphology Control Concluding Remarks
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.014 |
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".