Investigating Catechol Surface Modification of TiO<sub>2</sub> for the Development of High Performance Photoelectrochemical Biosensors
Bibliographic record
Abstract
DNA biosensors are chemical sensors which are used to detect concentrations of specific DNA sequences or register DNA related biological events. There is significant interest in research into the design and optimization of DNA biosensors as they allow for rapid detection of cancers and infectious diseases 1,2. Photoelectrochemical (PEC) techniques can be applied to achieve high performance DNA biosensors with low cost and ultra-sensitivity. In this work, we explored the surface modification of TiO2 nanoparticles (NP) with various catechol molecules and used them to fabricate photoelectrodes for signal-off PEC DNA biosensors. Catechol modification provided dual functionality to the TiO2 NP photoelectrodes: it enhanced their PEC response and provided a molecular linker for attaching probes that capture target DNA. The surface modification of TiO2 NPs was done by adding aqueous solutions of catechol molecules to a suspension of TiO2. Adsorption of catechol on TiO2 took place very rapidly, indicated by a quick change in the colour of the suspension. The catechol-modified TiO2 suspensions were dropdeposited on indium tin oxide coated polyethylene terephthalate (ITO/PET) substrates and oven baked to produce photoelectrodes for the PEC DNA biosensor. Seven catechol molecules were used for surface modification: Caffeic acid (CA), 3,4-Dihydroxybenzaldehyde (DHBA), 3,4-Dihydroxybenzoic acid (DHB), 3,4-Dihydroxy-L-phenylalanine (DOPA), 3,4-Dihydroxyphenylacetic acid (DHPL), 2,3,4-Trihydroxybenzaldehyde (THBA), 2,3,4-Trihydroxybenzoic acid (THB). The photoelectrodes were optically characterized via ultraviolet/visible (UV/vis) spectroscopy and incident photon-to-electron conversion efficiency (IPCE). Additionally, the PEC current of the photoelectrodes were measured using chronoamperometry under white light excitation. Compared against unmodified TiO2, the UV/Vis spectroscopy and IPCE displayed increased light absorption in the visible range and red-shifting for the catechol-modified TiO2 photoelectrodes. This correlated to a great improvement in photocurrent generation. CA-modified TiO2 resulted in the largest increase in photocurrent, over 20 times from the unmodified TiO2 and therefore it was used as the basis for our PEC DNA biosensor. Single stranded DNA (ssDNA) probes were deposited onto the photoelectrodes, followed by target ssDNA deposition. The difference in PEC current after probe deposition and target deposition indicated the concentration of target DNA. A calibration curve was plotted with PEC current density as a function of target DNA concentration, which decreased logarithmically and ranged from 100 nM – 100 fM. A limit of detection (LOD) of 2.38 pM was estimated at a signalto-noise ratio of 3Sx/y (where Sx/y is the root-mean-square error (RMSE)). It is evident from our investigation that functionalizing TiO2 with catechol ligands greatly improves its optical properties which lead to a greater PEC current generation. In particular, high PEC current resulting from the combination of CA and TiO2 can form the basis for high-performance signal-off PEC DNA biosensors with a high dynamic range and low LOD. 1. Beltrán, A. P. & García, M., DNA biosensors and biomarkers to cancer detection. International Journal of Biosensors and Bioelectronics, 4(1), 20-21 (2018) 2. Chua, A., Yean, C. Y., Ravichandran, M., Lim, B. & Lalitha, P., A Rapid DNA Biosensor for the Molecular Diagnosis of Infectious Disease. Biosensors and Bioelectronics, 26(9), 3825-3831 (2011)
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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 | 0.000 |
| 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) | 0.001 | 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 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".