Electroporation as an Optimizing Step in the Drying of Green Biomass
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".