Surface modification of TiO<sub>2</sub> for photoelectrochemical DNA biosensors
Bibliographic record
Abstract
Abstract A photoelectrochemical (PEC) DNA biosensor is developed using surface‐modified TiO2 nanoparticles (NPs) as a sensitive transducer. Different catecholates and gallates are used as sensitizers for TiO2 NPs. The molecules are adsorbed on TiO2 via the catecholate type bonding mechanism to enhance light absorption in the visible range. The adsorbed molecules act as charge transfer mediators and enhance photocurrent. Despite the similar bonding mechanism of the molecules, the TiO2 NPs exhibit significant differences in photocurrent. The modified TiO2 films showed photocurrent increase in the order: 3,4‐dihydroxy‐L‐phenylalanine < 2,3,4‐trihydroxybenzoic acid < 3,4‐dihydroxybenzoic acid < 2,3,4‐trihydroxybenzaldehyde < 3,4‐dihydroxyphenylacetic acid < 3,4‐dihydroxybenzaldehyde < caffeic acid. Testing results provide an insight into the influence of the structure and properties of the organic molecules on their adsorption and photocurrents of modified TiO2 films. The TiO2 NPs modified with caffeic acid are used for the fabrication of PEC DNA biosensor by forming photoelectrodes and immobilizing probe single‐stranded DNA on their surface. The caffeic acid‐modified TiO2‐based photoelectrodes offer the required signal magnitude to distinguish between complementary and non‐complementary DNA sequences in the 100 nM–1 pM DNA concentration range and with a limit of detection of 1.38 pM, paving the way towards PEC DNA sensing.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".