Tensegrity Prediction of E. coli Deactivation by Sonication Strategy

• Main Authors: Ahmad Kueh, Beng Hong, Isabel Lim, Fong, Achmad, Syafiuddin, Rosmina, Ahmad Bustami
• Format: Article
• Language: English
• Published: AKADEMIA BARU PUBLISHING (M) SDN BHD 2024
• Subjects: QC Physics; TA Engineering (General). Civil engineering (General); TD Environmental technology. Sanitary engineering
• Online Access: http://ir.unimas.my/id/eprint/46992/1/kueh24%20Tensegrity%20prediction%20of%20E.%20coli%20deactivation%20by%20sonication%20strategy.pdf; http://ir.unimas.my/id/eprint/46992/; https://www.akademiabaru.com/submit/index.php/ard/article/view/5568; https://doi.org/10.37934/ard.122.1.7184

Bacterial infections continue to pose a significant challenge to public health especially in clean drinking water production. Hence, it is crucial to have effective methods for their elimination. The successful deactivation of bacteria like E. coli through emerging sonic means depends on the identification of their resonant frequencies. However, the conventional approach of determining these frequencies through labor-intensive physical experimentation can be both time-consuming and expensive. This article introduces an innovative approach to determining the resonant frequency of E. coli using a specifically designed tensegrity model that incorporates spectral element formulation. This model, which conforms to the shape of E. coli, enables the calculation of resonant frequencies without the need for time-consuming simulations of element sensitivity. To address the challenges associated with rigid body motion, a small incremental operation is employed to compute the system determinant. The resonant frequencies obtained from the model are shown to align excellently with existing experimental findings. Furthermore, the model reveals that alterations in the geometry of E. coli have a substantial impact on the frequency of deactivation, while other parameters such as density have less influence. The proposed tensegrity model is a potent technique that can rapidly and accurately identify resonant frequencies, thereby enabling instantaneous and more efficient bacterial deactivation, especially during periods of health emergencies.