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Record W4410775082 · doi:10.1016/j.energy.2025.136824

A novel solar energy-based hydrogen generator integrated with battery storage

2025· article· en· W4410775082 on OpenAlexaff
Mehmet Gursoy, İbrahim Dinçer

Bibliographic record

VenueEnergy · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsHydrogen storageEnergy storageBattery (electricity)Solar energyNuclear engineeringHydrogenGenerator (circuit theory)Photovoltaic systemElectrical engineeringEnvironmental scienceMaterials scienceEngineering physicsEngineeringChemistryPhysicsThermodynamicsPower (physics)

Abstract

fetched live from OpenAlex

Renewable energy generation enhances environmental carbon reduction, limits global warming, and addresses rising energy demand. This study is designed to meet the community's energy needs by producing electricity and hydrogen through the utilization of solar photovoltaic (PV) systems, energy storage, a unique hydrogen generator, and an on-site utility facility. The energy demand is balanced by a novel mathematical model, resulting in improved power output for the system. System Advisor Model (SAM) is employed for the development and testing of models. The effect of solar radiation on hydrogen production rates exhibits a linear trend, varying from 48.5 g/s in the absence of solar radiation to 48.501 g/s at a standard intensity of 1000 W/m 2 , underscoring the significance of solar intensity. The system achieves a hydrogen production rate of 153.95 tons per year and produces approximately 143,963 GWh of electricity annually, demonstrating its high-performance potential and significant energy output capacity. The energy and exergy efficiencies of the overall system were determined to be 30 % and 34.6 %, respectively. • The integrated system consists of a PV plant and a hybrid hydrogen reactor. • Power and green hydrogen are produced by using a solar energy source. • The design achieves 30 % exergy and 34.6 % energy efficiency overall.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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 designBench or experimental
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

Citations3
Published2025
Admission routes1
Has abstractyes

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