Qualitative and Quantitative Assessment of Biodiesel Derived from Microalgae
Bibliographic record
Abstract
A modular experiment for upper-level undergraduate students is presented, featuring the product cycle and process evaluating the quality and quantity of biodiesel obtained from a microalgae feedstock grown under carbon-supplemented and nutrient-deprived conditions. Batch reactors are used to cultivate and harvest microalgae, which undergo acid-catalyzed in situ transesterification to produce biodiesel. Methods are presented to quantify yield and analyze products via gas chromatography–mass spectroscopy. With an awareness of the limited time available in a teaching laboratory, this experiment was designed and successfully implemented in an undergraduate biochemistry course over four sessions. Reflux reactions were performed in hermetically sealed microwave vials, eliminating water usage and reducing glass breakage. Learning objectives were assessed by postcontent quizzes, and written reports using a rubric based on Bloom’s taxonomy, results of which indicate a demonstration of the higher-order cognitive abilities of Evaluation and Analysis. The ability to biochemically engineer cultivation conditions to influence lipid production gives this experiment the potential to be offered as a course-embedded undergraduate research experience (CURE) project. This easily adoptable experiment offers an engaging opportunity for students to experience the technical and societal aspects of emerging biofuel technologies.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".