A Highly Sensitive Immunosorbent Assay Based on Biotinylated Graphene Oxide and the Quartz Crystal Microbalance
Bibliographic record
Abstract
A high-sensitivity flow-based immunoassay is reported based on a gold-coated quartz crystal microbalance (QCM) chip functionalized directly in the QCM without requiring covalent conjugation steps. Specifically, the irreversible adsorption of a biotinylated graphene oxide-avidin complex followed by loading of a biotinylated capture antibody is applied to avoid more complex conventional surface modification chemistries and enable chip functionalization and sensing all within the QCM instrument. The resulting immunosensors exhibit significantly lower nonspecific protein adsorption and stronger signal for antigen sensing relative to simple avidin-coated sensors. Reproducible quantification of rabbit IgG concentrations ranging from 0.1 ng/mL to 10 μg/mL (6 orders of magnitude) can be achieved depending on the approach used to quantify the binding with simple mass changes used to detect higher concentrations and a horseradish peroxidase-linked detection antibody that converts its substrate to a measurable precipitate used to detect very low analyte concentrations. Sensor fabrication and assay performance take ∼5 h in total, which is on par with or faster than other techniques. Quantitative sensing is possible in the presence of complex protein mixtures, such as human plasma. Given the broad availability of biotinylated capture antibodies, this method offers both an easy and flexible platform for the quantitative sensing of a variety of biomolecule targets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".