Evaluation of Nanoparticle–Ligand Distributions To Determine Nanoparticle Concentration
Bibliographic record
Abstract
The concentration of nanoparticles in solution is an important, yet challenging, parameter to quantify. In this work, a facile strategy for the determination of nanoparticle concentration is presented. The method relies on the quantitative analysis of the inherent distribution of nanoparticle-ligand conjugates that are generated when nanoparticles are functionalized with ligands. Validation of the method was accomplished by applying it to gold nanoparticles and semiconductor nanoparticles (CdSe/ZnS; core/shell). Poly(ethylene glycol) based ligands, with functional groups that quantitatively react with the nanoparticles, were incubated with the nanoparticles at varying equivalences. Agarose gel electrophoresis was subsequently used to separate and quantify the nanoparticle-ligand conjugates of varying valences. The distribution in the nanoparticle-ligand conjugates agreed well with that predicted by the Poisson model. A protocol was then developed, where a series of only eight different ligand amounts could provide an estimate of the nanoparticle concentration that spans 3 orders of magnitude (1 μM to 1 mM). For the gold nanoparticles and semiconductor nanoparticles, the measured concentrations were found to deviate by only 7% and 2%, respectively, from those determined by UV-vis spectroscopy. The precision of the assay was evaluated, resulting in a coefficient of variation of 5-7%. Finally, the protocol was used to determine the extinction coefficient of alloyed semiconductor nanoparticles (CdSxSe1-x/ZnS), for which a reliable estimate is currently unavailable, of three different emission wavelengths (525, 575, and 630 nm). The extinction coefficient of the nanoparticles of all emission wavelengths was similar and was found to be 2.1 × 10(5) M(-1)cm(-1).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".