Electrochemical Detection of Biomolecules for Agriculture and Farming Applications
Bibliographic record
Abstract
There is an increasing interest in developing efficient techniques to detect agri-based biomolecules and biomarkers to improve food production and quality. Electrochemical sensors and biosensors are desirable over conventional analytical techniques due to the advantages of instrumental simplicity, rapid response, high sensitivity, cost-effectiveness, and portability [1-4]. The development of nanomaterials has paved the way for their applicability in designing high-performance electrochemical sensing devices for agriculture and environmental applications. Hydroxyproline (Hyp) is a significant amino acid present in connective tissues and extracellular matrix in all animal cells. Hyp's quantitative analysis plays a vital role in the quality testing of chicken, swine, and beef meat in farming. In the animal farming industry, sodium metabisulfite (SMBS) is used as a feed additive to control the effect of deoxynivalenol (DON), a mycotoxin from Fusarium Species. The monitoring of SMBS is essential as its over-dosage may cause significant side effects like stomach upset or heaviness. In this presentation, the synthesis of gold nanoparticles and graphene-based nanocomposites, and their applications for the detection of Hyp and SMBS are highlighted. Our study has shown that gold nanoparticles exhibit high sensitivity (8.5 μA/μM cm -2 ), a low limit of detection (2.6 μM), and a wide linear range for the detection of Hyp. The fabricated gold and fluorine-doped reduced graphene oxide nanocomposites show high-performance for detecting SMBS in different digestive fluids such as stimulated salivary fluid and simulated gastric fluid. The roles of nanomaterials in the electrochemical sensing applications are also discussed. [1] V.S. Manikandan, B.R. Adhikari, A. Chen, Analyst, 143, 4537-4554 (2018). [2] Z. Liu, V.S. Manikandan, A. Chen,Curr. Opin. Electrochem., 16 , 127-133 (2019). [3] S. Durairaj, B. Sidhureddy, A. Chen, J. Electrochem. Soc., 167 , 167511 (2020). [4] J. van der Zalm, S. Chen, W. Huang, A. Chen, J. Electrochem. Soc. , 167 , 037532 (2020).
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 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".