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.
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".