Nano Additives in Cashew Nut Shell Liquid Biodiesel and Environment Emissions of Diesel Engine
Bibliographic record
Abstract
The developing countries are using non-edible oils for the production of biofuels, additives, or alternate fuels. The research article focused on the behavior study and analysis of cashew nut shell liquid (CNSL) biodiesel obtained by processing the cashew nut shell liquid. The work is carried out to derive the thermal-cracked (TC) -CNSL oil from Cardonal in the temperature range l50℃ to 400℃. The chemical functional groups are studied using Fourier-transform infrared spectroscopy (FTIR) and gas chromatography-mass spectrometry (GCMS) techniques. The TC-CNSL blended fuel performance is compared with diesel and neat biodiesel (B-100). The physicochemical properties of diesel, CNSL, and TC-CNSL biodiesel are estimated based on American Society for Testing and Materials (ASTM) standards. The 50 parts per million (ppm) Cerium Oxide Nanoparticles are added with TC-CNSL-B25, TC-CNSL-B50, TC-CNSL-75, and TC-CNSL-BJOO and processed in a single stroke diesel engine working at constant speed 1500-rpm. The blended fuel is analyzed based on environmental emission parameters in the diesel engines. The carbon monoxide (CO), carbon dioxide (CO2) hydrocarbon (HC) emissions of B-100 are reduced by 40.5%, 60.9%, and 30.7% respectively in comparison to diesel, at full load. The nitrogen oxide (NOx) emissions are increased by 13.26 % in B-100 in comparison to diesel, at full load. The smoke density is also observed decreasing in B-100 in comparison to diesel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".