Rational Design of Magnetic Micronanoelectrodes for Recognition and Ultrasensitive Quantification of Cysteine Enantiomers
Bibliographic record
Abstract
Abstract Driven by the urgent need for recognition and quantification of trace amino acids enantiomers in various biologic samples, we demonstrate for the first time an ultrasensitive electrochemical chiral biosensor for cysteine (Cys) based on magnetic nanoparticles (Fe3O4@PDA/CuxO) as electrode units. d-Cys-Cu2+-d-Cys formed in the presence of cysteine exhibits strong stability and a shielding effect on the redox current of indicator Cu2+, which can be used to quantify and recognize d-Cys by square wave voltammetry. Simultaneous detection of d-Cys and homocysteine (Hcy) is achieved in the presence of other amino acids, demonstrating an excellent selectivity of the sensor. Moreover, aided by the enrichment treatment effect of magnetic micronanoelectrodes, an ultrahigh sensitivity up to 102 μA μM–1 cm–2 was achieved, the detection limit is reduced to picomolar level (83 pM) for d-Cys and can be used for the recognition of cysteine enantiomers. The proposed method has been verified by real sample analysis with satisfactory results. The results highlight the feasibility of our proposed strategy for magnetic micronanoelectrode sensor, electrochemical recognition, and quantification of d-Cys, which can be more broadly applicable than that with traditional electrode structures and further advance the field of electrochemical sensors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".