Mechanics of Peeling Adhesives From Soft Substrates: A Review
Bibliographic record
Abstract
Abstract Understanding peeling behavior in soft materials is integral to diverse applications, from tissue engineering, wound care, and drug delivery to electronics, automotive, and aerospace equipment. These applications often require either strong, permanent adhesion or moderate, temporary adhesion for ease of removal or transfer. Soft adhesives, especially when applied on soft substrates like elastomer-coated release liners, flexible packaging films, or human skin, present unique mechanical behaviors compared to adhesives applied on rigid substrates. This difference highlights the need to understand the influence of substrate rigidity on peeling mechanics. This review delves into both energy- and stress-based analyses, where a thin tape with an adhesive layer is modeled as a flexible beam. The energy analysis encompasses components like the energy associated with tape deformation, kinetic energy, and energy lost due to interfacial slippage. The stress analysis, on the other hand, focuses on structures with thin, deformable substrates. Substrates are categorized into two types: those undergoing smaller deformations, typical of thin soft release liners, and thicker deformable substrates experiencing significant deformations. For substrates with small deformations, the linear Euler–Bernoulli beam theory is applied to the tape in the bonded region. Conversely, for substrates experiencing significant deformations, large deflection theory is utilized. These theoretical approaches are then linked to several practical, industrially relevant applications. The discussion provides a strategic guide to selecting the appropriate peeling theory for a system, emphasizing its utility in comprehending peeling mechanisms and informing system design. The review concludes with prospective research avenues in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".