Development of Novel Hybrid Silica Aerogels with a Reticulated Structure using New Generation of Marcromolecular Precursors
Bibliographic record
Abstract
Aerogels (e.g. silica aerogel) have created a prospective market for oil-absorption, insulation and aerospace applications. However, their intrinsic fragility and poor mechanical properties have been a stumbling block to their wider applications. These drawbacks originate from their inherent particulate structure and weak interparticle interactions. For several decades, researchers have been trying to enhance the aerogels’ mechanical properties by improving the particle-to-particle connections. However, the cost was increased processing time, increased density, and increased total thermal conductivity. Therefore, this Ph.D. research aims to find an industrially viable and fast manufacturing technology to produce ultra-light weight and mechanically robust and resilient aerogel while preserving the unique features of the aerogels such as the good thermal insulation properties and high surface area. This dissertation fundamentally addressed the difficulties associated with the improvement of the particle-to-particle connection in the conventional aerogels by inventing a novel non-particulate and reticulate structure, free from the troublesome particles. This new technology relied on the development of a new generation of polymeric silica precursor and a meticulous control of thermodynamics at gelation step. Polymeric precursors with different chemistries and molecular weights were synthesized in the preparatory stage and then through spinodal decomposition, non-particulate and reticulate aerogels were afforded. Thermodynamics variables, molecular weight of the polymeric precursor, solvent, temperature, and the amount of the nonsolvent were systematically studied and correlated to structural parameters and consequently to the final properties. It was found that there is a critical point where a particulate structure transfers to a non-particulate one. The findings have also demonstrated how to tailor the structural factors such as the void fraction, the average pore sizes to optimize the final properties. This study provided an in-depth understanding of processing-structure-property relationship and a novel technology to molecularly engineer the structure and properties of hybrid silica aerogels. Various aerogels’ building blocks assemblies, polymeric precursors, and thermodynamics variables have been investigated to identify the optimal structure and processing methodology to obtain highly robust and super-insulative aerogels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 teacher head, 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".