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Record W2133236814 · doi:10.1039/c4lc00267a

Energy: the microfluidic frontier

2014· article· en· W2133236814 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

VenueLab on a Chip · 2014
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrofluidicsNexus (standard)FrontierNanotechnologyLeverage (statistics)ScalabilityScale (ratio)Computer scienceMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Global energy is largely a fluids problem. It is also large-scale, in stark contrast to microchannels. Microfluidic energy technologies must offer either massive scalability or direct relevance to energy processes already operating at scale. We have to pick our fights. Highlighted here are the exceptional opportunities I see, including some recent successes and areas where much more attention is needed. The most promising directions are those that leverage high surface-to-volume ratios, rapid diffusive transport, capacity for high temperature and high pressure experiments, and length scales characteristic of microbes and fluids (hydrocarbons, CO2) underground. The most immediate areas of application are where information is the product; either fluid sample analysis (e.g. oil analysis); or informing operations (e.g. CO2 transport in microporous media). I'll close with aspects that differentiate energy from traditional microfluidics applications, the uniquely important role of engineering in energy, and some thoughts for the research community forming at the nexus of lab-on-a-chip and energy--a microfluidic frontier.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.688
Threshold uncertainty score0.806

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)0.0000.001

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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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