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Nottingham Trent University

Semen quality detection using acoustic wave sensors

Abstract

dc:description.abstract

Artificial insemination (AI) is a widely used part of the modern agricultural industry, with the number of animals inseminated globally being measured in the millions per anum. Crucial to the success of AI is that the sperm sample used is of a high Quality. Two factors which determine the quality of the sample are the number of sperm present and their motility. There are numerous methods used to analyse the quality of a sperm sample, but these are generally laboratory based, expensive and in need of a skilled operator to perform the analysis. It would, therefore be useful to have a simple and inexpensive system which could be used outside the laboratory, immediately prior to the insemination of the animal. Presented in this thesis is work developing a time of flight (ToF) technique which makes use of a quartz crystal microbalance (QCM), operating at 5 MHz, as the sensing element. Data is shown developing a device where a 50 μl sample of boar sperm is added to a liquid filled swim channel, which the sperm are allowed to self-propel down and attach to the surface of a QCM at the end. The attachment of the sperm to the surface causes a measurable frequency decrease in the QCM, aproximately 50 Hz. An average effective mass measurement was made using a QCM and gave a value of 8 ± 5 pg per sperm, which was used in conjunction with the frequency change to determine the number rate of sperm reaching the QCM.

Degree

thesis:*
Name dc:type.qualificationname
phd
Level dc:type.qualificationlevel
doctoral
Grantor dc:publisher.institution
Nottingham Trent University
Year dc:date.issued
2011

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Atherton, S

Rights

Language dc:language
en

Chain of custody

source
Harvested from
Nottingham Trent University
Base URL
irep.ntu.ac.uk/cgi/oai2
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
related terms
citation

Atherton, S. Semen quality detection using acoustic wave sensors. doctoral thesis, Nottingham Trent University, 2011.