Recent advances in graphene oxide-based electrochemical sensors
Bibliographic record
Abstract
Graphene oxide-based nanomaterials are garnering significant attention from various fields due to their unique physical and chemical properties, which make them attractive for myriad applications. This feature article stems from Dr. Aicheng Chen’s Lecture for the 2023 Ricardo Aroca Award of the Canadian Society for Chemistry and focuses primarily on the synthesis and applications of graphene oxide-based nanomaterials for electrochemical sensing. Graphene oxide (GO), reduced graphene oxide, and functionalized and doped GO-based nanomaterials are emerging as promising enablers of broad applications in electrochemical sensors. This is due to their distinctive functional groups and novel properties, which include high electrical conductivities, large surface areas, and enhanced catalytic activities. Several strategies for the synthesis of these nanomaterials are described in this feature article, encompassing the modified Hummers method, electrochemical, thermal, and microwave reactor methods. The characterization of these GO-based nanomaterials is highlighted, employing techniques such as scanning electron microscopy, transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, Raman, and X-ray photoelectron spectroscopy to study their morphologies, structures, and chemical compositions. The development of GO-based electrochemical sensors and their pharmaceutical, medical, environmental, and food safety applications are discussed. Further, the effects of these GO-based nanomaterials and different electrochemical techniques (e.g., cyclic voltammetry, square wave voltammetry, linear sweep voltammetry, and differential pulse voltammetry) on the performance of various electrochemical sensors are addressed.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".