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Record W2803840945 · doi:10.1149/ma2018-01/36/2141

Developing Wrinkled Surface to Achieve Low-Cost Photoelectrochemical Biosensor and Study the Interplay between LSPR of Nanoparticles and Semiconductive Quantum Dots

2018· article· en· W2803840945 on OpenAlexaff
Sudip Saha, Leyla Soleymani

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhotocurrentMaterials scienceBiosensorQuantum dotNanotechnologyOptoelectronicsPlasmonSemiconductorElectrodeNanoparticleLayer (electronics)PhotoelectrochemistryElectrochemistryChemistry

Abstract

fetched live from OpenAlex

The emergence of photoelectrochemical (PEC) biosensors has opened the door to achieve biosensors with a low limit-of-detection as PEC technique offers reduced background signals 1,2 . As a result, researchers are trying to use PEC technique to achieve low-cost and ultrasensitive biosensors. In this work, we proposed a novel method for fabricating flexible, low-cost PEC sensor substrates. Exploiting wrinkled surfaces, we have shown an increase in the photocurrent density of more than 700%. We investigated photocurrent density of CdTe Quantum dots (QD) on top of wrinkled and planar ITO surface. The quality of the substrates has been also verified by measuring sheet resistance and electron mobility. Moreover, these substrates, with an enhanced PEC response, have been used to investigate the interplay between localized surface plasmonic effects (LSPR) and photoactive semiconductor materials using DNA as spacer and gold nanoparticle as plasmonic materials. The wrinkled structure has been achieved by thermally shrinking (heating at 140°C for 5 minutes) pre-stretched PS substrates modified using various surface treatments. RF sputtering technique has been used to deposit ITO on planar and wrinkled PS. The thicknesses of the ITO film used for this study are 50nm and 100nm. Two methods have been used to prepare the wrinkled electrodes. In the first method, thermal shrinking has been done after putting ITO on PS, whereas for other method, PS substrates were cleaned using UV-Ozone (UVO) and then heated to shrink. ITO was sputtered on those shrunk substrates. CdTe QDs have been deposited using the layer-by-layer method. The photocurrent was measured by using chronoamperometry in a three-electrode setup illuminated using an LED excitation source. Scanning electron microscopy (SEM) has been used to visualize the structure of the electrodes. Cross-sectional Transmission electron microscopy (TEM) is used to probe the QD distribution in both planar and wrinkled ITO surface. To investigate the interplay between LSPR of metallic nanoparticles and the photo-activity of semiconductor quantum dots, a material architecture based on a DNA spacer was developed on the optimized wrinkled electrode. The length of DNA spacer was adjusted to tune the distance between the nanoparticles and the QDs. Substrates created by ITO deposition following the shrinking process showed the highest photocurrent density (more than 700% enhancement) among the analyzed samples. SEM images showed that when the substrates were heated after depositing ITO, it tends to break in places, which creates more surface defects, whereas more uniform ITO is obtained if the thin film is applied after wrinkling. TEM images showed much higher QD density on the wrinkled substrate compared to the planar one. By controlling the distance of the QD and Au-NP, the photocurrent can be enhanced or quenched. Quenching of the photocurrent have been observed when longer DNA was used as probe, whereas photocurrent enhancement was achieved by using shorter oligonucleotide. The substrates developed here are ideally suited for enhancing the limit of detection of biosensors. Moreover, this platform has enabled us to study the interaction between plasmonic nanoparticles and photoactive semi-conductive QDs to understand the underlying mechanisms for enhancing or quenching the photocurrent. Devadoss, A., Sudhagar, P., Terashima, C., Nakata, K. & Fujishima, A. Photoelectrochemical biosensors: New insights into promising photoelectrodes and signal amplification strategies. Journal of Photochemistry and Photobiology C: Photochemistry Reviews 24, 43–63 (2015). Fan, G., Han, L., Zhang, J. & Zhu, J. Enhanced Photoelectrochemical Strategy for Ultrasensitive DNA Detection Based on Two Different Sizes of CdTe Quantum Dots Cosensitized TiO 2 /CdS:Mn Hybrid Structure. Analytical Chemistry (2014). doi:10.1021/ac503043w

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.281
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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