Bifunctional MoS2-BiVO4 heterojunction boosts photoelectrochemical and colorimetric dual-mode sialic acid sensing
Bibliographic record
Abstract
Sialic acid (SA), a vital biomarker, plays a key role in diagnosing and monitoring various diseases. Herein, a dual-mode sensing system integrating photoelectrochemical (PEC) and colorimetric detection approaches was developed for the sensitive and selective detection of sialic acid in serum samples. This system offers cross-validated, independent signal readouts, thereby enhancing detection accuracy and reliability. For the first time, we unveil the dual functionality of the MoS 2 -BiVO 4 heterojunction, which significantly boosts both PEC and peroxidase-like nanozyme activities. This synergistic effect simplifies the construction of a dual-mode sensing system by utilizing a single bifunctional indicator rather than combining two separate indicators. A dual-functional MoS 2 -BiVO 4 heterojunction with extensive interfacial contact was synthesized via a templating strategy using flower-like MoS 2 to guide the growth of BiVO 4 nanoparticles, resulting in enhanced charge separation and catalytic activity surpassing those of the individual components. To ensure selective recognition, a sialic acid-specific molecularly imprinted polymer (MIP) was further deposited on the MoS 2 -BiVO 4 heterojunction, serving as the recognition element of the sensing system. The selective binding of sialic acid led to a decrease in both PEC and colorimetric signals, forming the basis for dual-mode detection. After optimizing preparation and detection conditions, the sensing system demonstrated excellent analytical performance, with a broad linear concentration range from 1×10 −11 M to 1×10 −6 M and a low detection limit of 3.4 × 10 −12 M. Its successful application to serum samples, with mutually validated dual-mode detection results, further confirmed the high accuracy and practical reliability of this dual-mode sensing system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".