Optical characterization of nanoparticle infused SiO? aerogels
Bibliographic record
Abstract
Windows play a crucial role in accounting for energy consumption through heat loss to the outdoor environment.This leaves room for improvement of window materials that improve energy efficiency and reduce greenhouse gas emissions.Although state-ofthe-art manufacturing techniques and approaches to solving window heat loss exist, the solution often comes as a trade-off between thermal resistance and light transmittance.Here, we present custom fabricated nano-particle infused aerogels as a potential material to achieve super insulating and transparent window panels.By incorporating nanoparticle solutions within the silicon dioxide (SiO) aerogel during the sol-gel process, unique monolithic samples were fabricated and optically characterized.Direct spectral transmittance of the fabricated samples between 200 and 7000 nm was measured using UV-Vis (Shimadzu UV-2600) and FTIR (Bruker Vertex 70) spectrometry.Optical measurements showed a peak transmittance of 96.20% at 1100 nm for the zinc-oxide (ZnO) infused SiO samples and 99.76% at 1239 nm for baseline SiO samples.Importantly, the results show that nanoparticle infusion can be used as a flexible method to tailor the optical properties of aerogels.In particular, the inclusion of nanoparticles within SiO2 aerogels is shown to reduce transmission (heat loss) in the mid-infrared (MIR) while maintaining high transmission in the visible wavelength region.Overall, this work serves as a proof-of-concept demonstration of how nanoparticle infusion can be utilized to tailor the optical behavior of the silica aerogel, opening the door for its use as a next generation high-performance window material.
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 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) | 0.000 | 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 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".