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Facile and Novel Strategy for Methods of Extraction of Biofuel Grade Lipids from Microalgae- an Experimental Report

2014· article· en· W2060593160 on OpenAlexvenueno aff
Nooruddin Thajuddin, E. Baldev, Davoodbasha MubarakAli, Masilamani Dhivya, M. Kanimozhi, Thajuddin Shakena-Fathima, Naiyf S. Alharbi, Sulaiman Ali Alharbi

Bibliographic record

VenueInternational Journal of Biotechnology for Wellness Industries · 2014
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsnot available
Fundersnot available
KeywordsBiofuelExtraction (chemistry)Biochemical engineeringChemistryBiotechnologyBiologyChromatographyEngineering

Abstract

fetched live from OpenAlex

The structural features of microalgal cell make it too difficult to extract the total lipid content of the cell as such. Thus, the cell disruption before lipid extraction becomes mandatory and has to be cost-effective. In the present study various methods and combination of few methods were adopted for effective extraction in order to choose the most effective cell disruption method for the complete extraction of lipids from a selected indigenous freshwater isolate, Scenedesmus sp. NTEB03. Interestingly, we found that grinding and bead-beating method showed two fold increased lipid productivity (23.2%) than the other methods tested. Biomass and lipid productivity of Scenedesmus sp., was found to be 0.0418 g L -1 d -1 and 4.3 mg L -1 d -1 respectively. Fatty acid profiles revealed that oleic (C18:1) and linoleic acid (C18:2) content being higher in the lipids, which are most appropriate for the biodiesel production. A novel strategy for most effective, simple method for cell disruption in Scenedesmus sp., was grinding/bead-beating, which is the most suitable method for complete extraction of biofuel grade lipids.

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)0.001
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 score0.214
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0010.000
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 head0.051
GPT teacher head0.362
Teacher spread0.311 · 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

Citations18
Published2014
Admission routes1
Has abstractyes

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