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Record W4401954743 · doi:10.18280/mmep.110822

Electrochemical Energy Generation by Reusing Domestic Gray Water

2024· article· en· W4401954743 on OpenAlexvenueno aff
Hugo Rivera-Aquino, Ciro Rodríguez, Julio Cesar-Minga, Diego Rodriguez

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
FundersUniversidad Nacional Mayor de San Marcos
KeywordsGreywaterEnvironmental scienceReuseEnvironmental economicsEnergy conservationEfficient energy useSustainabilityScalabilityAlternative energySustainable energyComputer scienceEnvironmental engineeringProcess engineeringEngineeringWastewaterWaste managementRenewable energyElectrical engineeringEcology

Abstract

fetched live from OpenAlex

<p>This study explores the potential of converting domestic graywater into electrochemical energy as a sustainable energy solution amidst growing environmental concerns. Employing a custom-designed galvanic cell prototype, the research aims to transform the chemical energy in graywater into electrical energy through redox reactions, quantifying the electrical potential generated. Results demonstrate the prototype's success in generating an average no-load voltage of 5.1907 volts, effectively powering low-power devices like LEDs and validating the viability of greywater as an alternative energy source. However, the study acknowledges limitations such as the prototype's small scale and the potential impact of varied graywater compositions on energy efficiency, suggesting cautious application at larger scales. Future research directions include enhancing prototype efficiency and scalability, understanding the effects of different graywater compositions, and conducting long-term performance assessments. The study contributes to sustainable energy research by offering a novel approach to wastewater energy recovery, promoting environmental sustainability and efficient energy utilization.</p>

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.703
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.183
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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