Electrochemistry of mitochondrial isolates on ultrasonicated graphene Oxide-modified electrodes
Bibliographic record
Abstract
Mitochondria are central to cellular energy production and metabolic regulation, and their dysfunction is linked to various diseases. Understanding mitochondrial activity through electrochemical studies may provide valuable insights into their function, but direct characterization remains challenging due to the complexity of the electron transport chain (ETC) and the need to maintain mitochondrial integrity. While pyrolytic graphite (PGE) and carbon paper electrodes have been used in the past, the inherently weak and poorly resolved voltammetric signals from mitochondria complicate their electrochemistry. This study investigates the use of PGE modified with graphene oxide obtained by electrochemical exfoliation of graphite (EGO) to enhance the electrochemical signals of isolated human mitochondria. To assess such activity, square wave voltammetry (SWV) was conducted in physiological conditions and compared with metabolic assays. The EGOs flakes and their suspensions obtained after different sonication times, and used in the fabrication of the electrodes were characterized by Scanning electron microscopy, X-ray photoelectron spectroscopy, transmission electron microscopy (TEM), and UV-vis spectroscopy. TEM confirmed mitochondrial structural integrity after interaction with the EGO. Sonication time plays a critical control in mitochondrial viability and activity, as prolonged sonication yields smaller EGO flakes and more graphene oxide quantum dots (GOQD). The flakes improved the interaction between the mitochondria and the electrode’s surface, whereas the GOQD facilitated the electron transfer between the ETC and the electrode, leading to stronger electrochemical signals. We highlight the importance of using SWV in combination with EGO and GOQD to resolve these signals more effectively, overcoming the limitations of typical voltammetry tests.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".