Three-Dimensional Copper Foam Nanocatalysts for Nonenzymatic Electrochemical Lactate Sensing
Bibliographic record
Abstract
Copper foam annealed in oxygen at a relatively low temperature has been found to exhibit remarkable catalytic activities, with enhanced electrochemically active surface area, high conductivity, and distinct surface structures, appropriate for nonenzymatic lactate sensing. X-ray photoelectron spectroscopy and X-ray diffraction data show that these annealed Cu foam exhibit several Cu-oxidation states, and a morphology consisting of a metallic Cu base, a CuO layer on the surface, and an intermediary Cu 2 O layer in between. The relative compositions of Cu 2 O and CuO layers are found to vary with the annealing temperature, resulting in differences in the conductivity and catalytic properties of the as-prepared samples. In particular, the Cu foam annealed in oxygen at 200 °C for 60 min exhibits high conductivity and high charge carrier concentration, as determined by electrochemical impedance spectroscopy and Hall effect measurement, respectively. High specific surface area is also shown, in accordance with the electrochemically active surface area calculation. For nonenzymatic lactate sensing, this annealed Cu foam sample is found to exhibit an excellent linear range of 1–120 mM, a high sensitivity of 800 μA mM –1 cm –2, and a low limit of detection of 0.367 μM. When tested for lactate sensing in an artificial sweat electrolyte, this sample shows the same excellent linear range (1–120 mM), but a higher limit of detection of 1.807 μM, while maintaining a somewhat reduced sensitivity of 680 μA mM –1 cm –2 . This work demonstrates that manipulating Cu foam consisting of an appropriate 3D framework with an inherently high surface area is a promising strategy to develop advanced nanocatalysts for lactate and other biochemical sensing.
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) | 0.001 | 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".