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Record W4406534917 · doi:10.1002/admi.202400680

Connecting Droplet Adhesion with Sperm Kinematics: A New Paradigm in Sperm Quality Monitoring

2025· article· en· W4406534917 on OpenAlexafffund
Sudip Shyam, Sirshendu Misra, Veronika Magdanz, Sushanta K. Mitra

Bibliographic record

VenueAdvanced Materials Interfaces · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpermMaterials scienceAdhesionKinematicsSperm qualityNanotechnologyComposite materialBiologyPhysicsBotanyClassical mechanics

Abstract

fetched live from OpenAlex

Abstract Conventional microscopy‐driven sperm health monitoring systems suffer from high infrastructural costs and complex protocols. Here, a simple, economical sperm motility assessment system is proposed. Based on a cantilever‐deflection‐based direct force measurement system capable of detecting wetting forces in the range of ≈µN, it is found that the adhesion of live and motile sperm cell‐laden droplets is dependent on sperm motility in the suspension. Further, it is observed that the sperm motility inside the droplet decreases with time, and the adhesion of the concerned droplet with a master substrate demonstrates an increasing trend. Contrary to an immotile cell, the motile sperm, due to its inherent nature of swimming parallel to the contact line, induces a lower restrictive force on the receding triple contact line of the droplet. The study establishes a potential avenue by which sperm cell motility can be predicted via measuring the adhesion of the sperm‐cell‐laden droplets with a standard surface using the simple and automatable cantilever‐deflection method. These findings can pave a pathway toward developing a user‐friendly, expertise‐independent diagnostic platform for in‐house sperm health monitoring.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.313
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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