Detection of gold cysteine thiolate complexes on gold nanoparticles with time-of-flight secondary ion mass spectrometry
Bibliographic record
Abstract
Gold (Au) nanoparticles (NPs) are widely used in nanomedical applications as a carrier for molecules designed for different functionalities. Previous findings suggested that biological molecules, including amino acids, could contribute to the dissolution of Au NPs in physiological environments and that this phenomenon was size-dependent. We, therefore, investigated the interactions of L-cysteine with 5-nm Au NPs by means of time-of-flight secondary ion mass spectrometry (ToF-SIMS). This was achieved by loading Au NPs on a clean aluminum (Al) foil and immersing it in an aqueous solution containing L-cysteine. Upon rinsing off the excessive cysteine molecules, ToF-SIMS confirmed the formation of gold cysteine thiolate via the detection of not only the Au-S bond but also the hydrogenated gold cysteine thiolate molecular ion. The presence of NaCl or a 2-(N-morpholino)ethanesulfonic acid buffer disabled the detection of Au NPs on the Al foil. The detection of larger (50-nm) Au NPs was possible but resulted in weaker cysteine and gold signals, and no detected gold cysteine thiolate signals. Nano-gold specific adsorption of L-cysteine was also demonstrated by cyclic voltammetry using paraffine-impregnated graphite electrodes with deposited Au NPs. We demonstrate that the superior chemical selectivity and surface sensitivity of ToF-SIMS, via detection of elemental and molecular species, provide a unique ability to identify the adsorption of cysteine and formation of gold-cysteine bonds on Au NPs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".