Quantification of gold nanoparticles in biological samples with total reflection x-ray fluoresence
Bibliographic record
Abstract
The applications of gold nanoparticles (AuNPs) in biomedicine span the imaging, diagnosis and therapy domains. Current methods of nanoparticle quantification are based on inductively coupled plasmas (ICP) methods, which are heavily influenced by sample preparation. This becomes challenging when quantifying trace-level amount of AuNPs in the presence of organic matrix. In this thesis, total reflection X-ray fluorescence (TXRF) spectrometry is proposed for quantifying cellular uptake of AuNPs in MDA-MB-231 breast cancer cell line suspensions. The most suitable internal standard and fitting approach that yield 90-110% recovery rate along with different sample preparation methods were investigated. Reference material AuNPs were used to validate the quantification capabilities of the newly developed method. Direct comparison of TXRF and ICP-AES showed ability of TXRF to accurately quantify nanoparticle uptake while further demonstrating the importance of sample preparation for ICP-AES. These results suggest that TXRF has potential for quantifying nanoparticle uptake and their kinetics.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".