Photothermal Effect of PEG-Functionalized Single-Walled Carbon Nanotubes
Bibliographic record
Abstract
Background and objective: There has been a dramatic increase in photothermal therapy as a minimally invasive treatment modality for cancer therapy due to the development of novel nano-materials as the light absorbing agents.PEG-functionalized Single-Walled carbon Nanotubes (PEG-SWNTs) with strong optical absorption in the broad visible and Near Infrared (NIR) waveband offer unique advantages for photothermal cancer therapy.The objective of this study is to investigate the rule of PEG-SWNTs in absorbing 785 nm NIR laser light and generating hyperthermia. Materials and methods:Eight concentrations of PEG-SWNTs solutions were prepared by 2-fold serial dilution at concentrations of 1.0, 0.5, 0.25, 0.125, 0.0625, 0.0312, 0.0156 and 0.0078 mg/ml.The solvent without PEG-SWNTs was used as control.The temperature changes of the PEG-SWNTs solution during laser irradiation were monitored with an infrared thermometer.Three levels of laser power densities were chosen for the treatment including 100, 200 and 500 mW/cm 2 . Results and conclusion:The temperature of PEG-SWNTs solution increases linearly with laser power density, but nonlinearly with irradiation time and concentration.The solution temperature increases much faster within the first 3 minutes of irradiation and then gradually levels off as the irradiation approaching 10 minutes.The maximum temperature change (∆T max ) in each solution increases sharply with increasing PEG-SWNTs concentration up to 0.125 mg/ml and eventually levels off beyond 0.25 mg/ml.Based on this observation, optimized treatment parameters (laser power density and SWNT concentration) could be obtained to raise the temperature by 10-30˚C sufficiently for causing cell apoptosis and/or necrosis.For in vitro cellular and in vivo tissue studies, similar experiments could be carried out to find the optimal treatment parameters.
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 | 0.000 | 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 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".