Structure, Function, and Application of Self‐Healing Adhesives from Mistletoe Viscin
Bibliographic record
Abstract
Abstract Berries from the European Mistletoe ( Viscum album ) possess a sticky tissue called viscin that facilitates adhesion and germination onto host trees. Recent studies of viscin have demonstrated its adhesive capacity on a range of natural and synthetic surfaces including wood, skin, metals, and plastic. Yet, the underlying mechanisms remain poorly understood. Here, an investigation of the adhesive performance of mistletoe viscin is performed, demonstrating its hygroscopic nature and ability to self‐heal following adhesive failure. It is identified that adhesion originates from a water‐soluble adhesive component that can be extracted, isolated, and characterized independently. Lap shear mechanical testing indicates that the mistletoe adhesive extract (MAE) outperforms native viscin tissue, as well as gum arabic and arabinogalactan—common plant‐based adhesives. Furthermore, humidity uptake experiments reveal that MAE can reversibly absorb nearly 100% of its mass in water from the atmosphere. In‐depth spectroscopic and mass spectrometry investigations reveal a composition consisting primarily of an atypical arabinogalactan, with additional sugar alcohols. Finally, several proof‐of‐concept applications are demonstrated using MAE for hygro‐responsive reversible adhesion between various surfaces including skin, plastic, PDMS, and paper, revealing that MAE holds potential as a biorenewable and reusable adhesive for applications in cosmetics, packaging, and potentially, tissue engineering.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".