Bibliographic record
Abstract
In this thesis, we introduce a new optical method based on gold nanoparticles as individual sensing platforms for the detection of low concentrations of analytes (DNA or proteins). Here we provide the proof of principle of the methodology in the detection of immunoreactions and determine its limit of detection. We show that with a simple colour camera we are able to detect simultaneously immunobinding on thousands of individual gold nanoparticles [92], by measuring the change in the colour of many individual nanoparticles [93]. If an amplification step is used the LOD of such a system can be boosted even more [94]. In order to get the best sensing strategies an understanding of the physical background is needed. Chapter 2 presents a review of the physical aspects of LSPR and continues with a theoretical discussion on the sensing strategies employing individual gold nanoparticles as sensing platforms. The concept of parallel detection of multiple individually addressable nanoparticles is introduced and a theoretical LOD is determined. Chapter 3 introduces a new detection method of binding events using a colour camera in a DF setup. The feasibility of this approach was tested in an adsorption assay and an immunoassay and the LOD of the method for this sensing strategy was experimentally measured. In addition, two different detection approaches are tested and their performances compared. In Chapter 4, we introduce a new sensing strategy using gold nanoparticle probes in microfluidic cells in colorimetric darkfield microscopy enabling the simultaneous sensing of hundreds of binding events of individual particles simultaneously. Here, single binding events can be observed and the results were confirmed by independent methods like Scanning Electron Microscopy. After demonstrating the proof of principle of our detection method in an unamplified protein assay and direct immunoassay, in Chapter 5 we test our setup in an amplified protein assay and determine the performances of the setup by measuring experimentally the LOD of the system. In Chapter 6, we review the main achievements presented in this thesis, and we present several future recommendations for this line of research.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.034 | 0.013 |
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".