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Record W4386005884 · doi:10.22214/ijraset.2023.55388

Design and Fabrication of Solar-Powered Portable Thermoelectric Refrigeration System for Thermolabile Pharmaceuticals

2023· article· en· W4386005884 on OpenAlexaff
Rutvik Mehenge, Aksh Patel, Pathan Shahidkhan

Bibliographic record

VenueInternational Journal for Research in Applied Science and Engineering Technology · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsTrinity College
Fundersnot available
KeywordsRefrigerationPhotovoltaic systemThermoelectric coolingElectricityEnvironmental scienceThermoelectric effectSolar energyThermolabileProcess engineeringElectrical engineeringEngineeringMechanical engineeringChemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract: In order to maintain the therapeutic efficacy of thermolabile medicine and vaccines, electrical energy must be continuously supplied. For remote areas lacking access to electrical electricity, this poses a serious dilemma. The potency level of thermolabile medications and vaccines is decreased, particularly during last-mile delivery, which causes significant financial loss. In this study, we designed and developed a portable active refrigeration system that runs on solar photovoltaic cells for the refrigeration of thermolabile pharmaceuticals to be utilized in rural areas without access to electricity, especially to facilitate last-mile vaccination delivery. The system makes use of a thermoelectric refrigeration system that, when given electrical power, causes a temperature difference based on the Peltier effect. It will be demonstrated that a solar panel with a peak power of roughly 50W and batteries with a storage capacity of 10Ah are needed for a typical application for vaccine refrigeration. The developed refrigeration system has a 3-liter volume capacity and can store 150 vaccine ampules, each with a 10ml capacity, at temperatures between 2°C to 8°C using a Peltier cell (TEC), that consumes 66 W at 12V

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)0.005
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.071
GPT teacher head0.400
Teacher spread0.330 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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