Biodegradable Alternatives to Plastic in Medical Equipment: Current State, Challenges, and the Future
Bibliographic record
Abstract
The use of plastic products or components in medical equipment and supplies results in challenges in terms of environmental sustainability and waste management for disposable, non-recyclable, and non-biodegradable materials. Medical plastic waste includes items ranging from syringes, tubing, intravenous (IV) bags, packaging, and more. Developing biodegradable replacements to petroleum-based plastics in medical equipment has not yet become an urgent priority, but it is an important endeavor. Examining alternatives involves several key themes, including material selection, testing, validation, and regulatory approval. To date, research includes studies on biodegradable polymers, composite materials, surface modifications, bacterial cellulose, three-dimensional (3D) printing with biodegradable materials, clinical trials and testing, collaboration with industry, regulatory considerations, sustainable packaging for medical devices, and life cycle analysis. The incorporation of bio-based and biodegradable plastics in the healthcare industry holds immense potential for reducing the environmental impact of medical plastic waste. The literature suggests that researchers and industry professionals are actively working towards finding sustainable alternatives that meet the stringent requirements of the medical industry. This paper reviews the efforts made so far to develop biodegradable and sustainable alternatives to plastic in medical equipment using a meta-analysis of resources, which include relevant papers published in English until June 2024. A total of 116 documents were found and screened by three reviewers for relevance. The literature reviewed indicated that various medical uses require plastics due to their unique properties, such as having strength and flexibility; being lightweight; and being able to prevent bacterial contamination. Among the alternatives, polycaprolactone (PCL), polylactic-co-glycolic acid (PLGA), starch-based acid, and polybutyric acid (PBS) have demonstrated favourable outcomes in terms of biocompatibility, safety, and efficacy. Additionally, a set of approaches to overcome these barriers and strategies is discussed alongside potential future solutions. This review aims to catalyze discussions and actions toward a more environmentally sustainable future in the medical industry by providing a comprehensive analysis of the current state, challenges, and prospects of this domain.
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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".