BIOPRODUCTS OF AUTOMOTIVE ACCESSORIES: RETHINKING DESIGN MATERIALS THROUGH CORNSTARCH, SUGARCANE AND HEMP
Bibliographic record
Abstract
Current bioproducts or bio-based products do not only require less energy to produce than petroleum-based products, they are made with renewable sources that engineered from excessive waste and natural local materials. This paper identifies alternative design solutions by suggesting a use of natural materials such as cornstarch, sugarcanes and hemp in designing automotive accessories. Leading automotive industries have focused on using bio-based materials for possible vehicle details such as dashboard panels, finishing trims and optional features and casing for light covers. A fermentation broth derived from cornstarch and sugarcanes, which were recovered as Polylactic acid or Polylactide (PLA), were selected by designer and automotive engineer to reconstruct bio-based materials to improve identity of bio-based design for an automotive world. This choice of material process yields similar quality to materials made from thermoplastic or materials categorized as lightweight-metal. Additional design examples of bio-based materials are products made from hemp fiber for bus seat in Canada, and biodegradable phone casing from England and Japan. These examples are described as solutions, which show sustainable use of alternative materials and suggest design applications that reflect concerns for the environment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".