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Record W2143347356 · doi:10.1109/ppps.2007.4345824

Electroporation as an Optimizing Step in the Drying of Green Biomass

2007· article· en· W2143347356 on OpenAlexaboutno aff
Martin Sack, C. Eing, L. Buth, Th. Berghöfer, Wolfgang Frey, H. Bluhm

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyBiomass (ecology)Raw materialProcess engineeringPulp and paper industryEnvironmental scienceSugarWaste managementAgricultural engineeringEngineeringChemistryAgronomyElectrical engineeringFood science

Abstract

fetched live from OpenAlex

Summary form only given. The replacement of exhaustive energy resources by renewable energy resources nowadays becomes an important field of work for our future energy supply. There are already some examples for the use of plants as a source of renewable energy, e.g. ethanol made from sugar cane and sugar beets to power spark ignited engines or rape seed oil for diesel engines. Moreover, new processes like the BIOLIQ-process enable the adaptation of fuel to the needs of the engines or to synthesize raw substances for the production of polymers. For the BIOLIQ-process dry biomass is required. Hence, the amount of raw material can be increased by drying of green biomass, e.g. whole maize plants (Zea metis). During the last few years the electroporation of plant cells became an interesting new method for an energy efficient denaturisation of plant cells. During the electroporation process high-voltage pulses are applied to the plant cells. The electric field set up across the cell membranes causes the formation of pores. Examples are the electric treatment of apples to increase the yield of juice, or the electroporation of cossettes of sugar beets in order to save energy compared to the conventional thermal process. For the mentioned applications of electroporation the plant material is immersed in water in order to guarantee a good contact to the electrodes. But for a drying process it is essential to omit the use of additional water. Hence, it has been tested in laboratory scale experiments, to use the water inside the plants only: slices of plant material are pressed before the electroporation, until the space between the plant material and the electrodes is filled with juice. After a second pressing step, the plant material is dried in an oven. The energy required for the electroporation and the drying process has been evaluated and compared to the energy required for the drying process without electroporation. A clear advantage in energy saving for the drying proc

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.020
GPT teacher head0.333
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2007
Admission routes1
Has abstractyes

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