A New Test Method to Characterize the Grip Adhesion of Protective Glove Materials
Bibliographic record
Abstract
Protective gloves can reduce the number and severity of hand injuries but may also impair manual performance such as grip strength. A worker wearing poorly adherent gloves must apply additional muscular effort to retain the handled parts, leading to discomfort, pain, and possibly musculo-skeletal disorders. Glove grip depends on the coefficient of friction (COF) between the glove and handled object surfaces. Knowledge of COF values can be helpful to workers for selecting suitable gloves. However, existing standard test methods for measuring COF are not applicable to gloves. In a previous study, a test method was proposed to evaluate COF of glove materials, using a modified version of the TDM-100 test apparatus initially designed to characterize the cut resistance of protective materials. The test method consists in sliding a flat metal probe on a flat material specimen at a constant speed while applying a load perpendicular to the contact surface. The friction force is measured with a load cell attached to the probe. The friction force versus displacement curve typically presents an initial peak representing the static COF, followed by a plateau representing the dynamic COF. In this study, the effect of the applied load and probe roughness on the COF was characterized with neoprene and nitrile rubber as well as with six different thermoplastic materials. The study demonstrates that both of them affect the measured static and dynamic COFs. A normal load of 10 N, and a probe surface roughness of 1.0 or 2.0 μm gave the most reproducible results and seemed to be the more appropriate to perform testing with different polymer materials. The coefficient of variation of both static and dynamic COFs values obtained was generally lower than 15 %. This test protocol demonstrates to be well adapted to characterize the grip adhesion of glove materials.
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 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) | 0.004 | 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 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".