Fabrication and characterization of silica aerogel as synthetic tissues for medical imaging phantoms
Bibliographic record
Abstract
Medical imaging plays an important role in the field of healthcare industry both in clinical settings and in research and development. It is used in prevention, early detection of disease, in choosing the optimal treatment, during surgical interventions and monitoring of the treatment effects. Despite much advancement in the last few decades, rapid change on its technology development and variety of imaging parameters that differ with the manufacturer restrict its further development. Imaging phantom is a calibrating medium that is scanned or imaged in the field of medical imaging to evaluate, analyze and tune the performance of various imaging devices. A phantom used to evaluate an imaging device should respond in a similar manner to how human tissue and organs would act in that specific imaging modality. There has been many research on the phantom materials; however, there has been no attempt to study on the material that mimics the structure of lung or fibrous tissue. So with the need for development of gel with such structure, we tried to mimic this structure with aerogel. Silica aerogels have unique properties that include low density (0.003g/cm) and mesoporosity (pore size 2-50nm), with a high thermal insulation value (0.005W/mK) and high surface area (500-1200m−2/g).] In this study, we cross-linked with di-isocyanate, which is a group in polyurethane to covalently bond the polymer to the surface of silica aerogel to enhance the mechanical properties. By formation of covalent bonds, the structure can be reinforced by widening the interparticle necks while minimally reducing porosity.
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) | 0.000 | 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".