Supported NiO<sub><i>x</i></sub> Nanocatalysts on Graphene for Nonenzymatic Lactate Sensing
Bibliographic record
Abstract
Electrodeposition of nickel nanoparticles, achieved through potentiostatic amperometry followed by thermal annealing at 200–400 °C, has produced nickel oxide nanoparticles on flexible graphene substrates. Using transmission electron microscopy and depth-profiling X-ray photoelectron spectroscopy, we demonstrate that the resulting NiO x nanoparticles exhibit several Ni oxidation states and a core–shell heterostructure, with a metallic Ni crystalline core and a NiO crystalline shell with a mixed crystalline–amorphous NiOOH/Ni(OH) 2 skin. The composition of the NiOOH/Ni(OH) 2 redox couple relative to NiO is found to vary with the annealing temperature and annealing time. The use of a highly conductive graphene substrate enhances electron transfer. The NiO x nanoparticle samples obtained with selected annealing temperature–time combinations are used for lactate detection, with the best sample showing an excellent linear range of 0.02–65.1 mM, a high sensitivity of 80.0 μA mM –1 cm –2, and an impressive limit of detection of 0.00015 mM. The NiO x nanoparticle sample is also tested for lactate sensing in an artificial sweat electrolyte, and it exhibits a reduced linear range of 0.02–53.1 mM and a lower limit of detection of 0.00013 mM while maintaining the same high sensitivity of 80.0 μA mM –1 cm –2 . This sensing performance can be optimized by controlling the relative composition NiOOH + Ni(OH) 2 to NiO, with the sample obtained with a higher relative content at a lower annealing temperature found to provide more reactive sites for lactate detection and therefore higher sensitivity.
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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".