Bibliographic record
Abstract
O arrays of metallic nanoholes have demonstrated great potential as sensors for the detection of analytes, including biomarkers for early diagnosis of diseases and viruses. These nanoplasmonic structures have been integrated into microfluidic environments in lab-on-chip formats towards the development of commercial-competitive biochemical diagnostics. The use of nanohole arrays in flow-through fashion has demonstrated additional benefits in terms of transport, such as targeted analyte delivery to the active sensing surface, effective analyte utilization and faster response times. In many applications, however, sensing must be achieved using samples with very low concentration of the target analyte. In order to overcome this challenge, concentration and purification stages are commonly employed before sensing. Among different approaches for achieving analyte preconcentration, electrokinetic techniques have demonstrated suitable for on-chip integration. The metallic nature of nanohole arrays offers the possibility to extend their use as active concentrators through electric field gradient focusing (EFGF) and the utilization of a pressure bias. Here we present the additional capabilities of the optofluidic structures to tailor the final concentration and spatial location of the analyte within the microfluidic chip, and the possibility to achieve transport control of the concentrated analyte using the nanoplasmonic structure as active switch. These demonstrated abilities extend the potentials of metallic nanohole arrays to achieve transport, concentration, active control and sensing using the same nanoplasmonic structure. Carlos Escobedo, J Phys Chem Biophys 2013, 3:5 http://dx.doi.org/10.4172/2161-0398.S1.004
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".