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Record W4409076301 · doi:10.1016/j.coche.2025.101122

Perspective in the industrial applications of sonoelectrochemical hydrogen production

2025· article· en· W4409076301 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Opinion in Chemical Engineering · 2025
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPerspective (graphical)Production (economics)Biochemical engineeringHydrogen productionProcess engineeringNanotechnologyEngineeringManufacturing engineeringSystems engineeringHydrogenComputer scienceChemistryMaterials scienceEconomicsArtificial intelligenceOrganic chemistry

Abstract

fetched live from OpenAlex

Sonoelectrochemistry is the incorporation of power ultrasound in electrochemistry. The use of ultrasound in electrochemical processes such as water electrolysis can lead to an energy efficiency enhancement in the range of 2–25% in low-temperature water electrolysers (LT-WE). However, this improvement greatly depends upon several factors such as the cell reactor design, the ultrasonic frequency, the transmitted acoustic power, and the distance between the ultrasonic transducer and the electrode. The main objectives of this review are to highlight recent advancements in using power ultrasound in water electrolysis and shed some light on possible commercial development by addressing the fundamental obstacles that lie in this technology. Several research works have highlighted that the efficiency improvement in ultrasound-aided water electrolysis is principally due to the gas bubble removal from the electrode surface, which ultimately reduces the ohmic resistance of the electrolytic cell. However, even with the observed higher efficiencies from the sonoelectrolysers for hydrogen production in R&D labs, this technology still faces challenges for further development due to the efficiency in competing with commercial LT-WEs, which are already in the range of 60–70%. If sonoelectrolysers are to succeed for commercial development and large-scale industrial applications, they would need to achieve overall efficiency much higher than current commercial LT-WEs. • Sonoelectrochemical hydrogen production is reviewed. • Power ultrasound enhances electrochemical hydrogen production by 2–25%. • The energy efficiency gain depends on the ultrasonic parameters and the cell design. • Power ultrasound cannot be used in zero-gap cells due to their compact designs. • Direct sonication by high-frequency transducers is suitable for industrial use.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.255

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 head0.024
GPT teacher head0.297
Teacher spread0.273 · 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