Analyzing the Properties of Saliva to Act as a Viable Fuel Source and Its Capabilities to Interact with Microbial Fuel Cell (MFC) Powered Theoretical Diagnostic Devices Beneficial to Low-income Communities
Bibliographic record
Abstract
Introduction: The WHO has stated that about 50% of the world lacks access to secure and continuous supply of electricity, heavily impacting the healthcare industry in these countries. Microbial Fuel Cells (MFCs) can be a low cost-efficient energy source capable of powering medical devices in low-income countries. Due to the components and impurities found in saliva, this biofluid can behave like an electrolyte and a viable fuel source to power the MFC. With this capability, saliva has the potential to power micro-gadgets with microbial fuel cells capable of degrading the components of saliva. Thus, this study explores saliva’s potential to act as a fuel source to power microbial fuel cells within medical diagnosis devices. Methods: A systematic review was conducted through primary and secondary research articles exploring and comparing the use of saliva as an energy source compared to other biofluids. Key terms focused for meta-analyses include: ‘semiconductors’, ‘saliva’, ‘microbial fuel cells’, ‘point-of-care’. Results: Previous research has discovered that lysozyme enzymes present in saliva can create an electrical charge that can successively power biomedical devices. Researchers have also created paper-based batteries containing frozen exoelectrogenic cells, powered by the bacterial degradation of human spit. Saliva has been demonstrated to contain similar biomarkers to urine, a successful diagnostic biofluid, and can therefore be used as a diagnostic biofluid as well. Discussion: Given saliva’s capabilities, a hypothetical diagnostic device powered using saliva as the biofluid, was designed. Bacteria break down the saliva, allowing protons to travel from the anode to the cathode resulting in electricity. It was determined that graphite would be the most cost-efficient and energy producing electrode material for the device. In addition, this electricity that is produced will power the diagnostic device attached. Conclusion: Saliva can act as a fuel source, capable of powering diagnostic devices using microbial fuel cells with saliva. These properties can be beneficial to many people who do not have access to preliminary diagnosis. This can result in immediate treatment and help prevent further spread of diseases, vital for those in low-income countries. Broad scale applications of using saliva can be directed towards exterior lighting systems and powering larger medical devices.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".