Micromachines 2022, 13, 1198. https://doi.org/10.3390/mi13081198 www.mdpi.com/journal/micromachines Article A Technique for Rapid Bacterial-Density Enumeration through Membrane Filtration and Differential Pressure Measurements Xinhui Shen † , Ting Wei Teo † , Tian Fook Kong and Marcos * School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore; shenxh@ntu.edu.sg (X.S.); teotw@ntu.edu.sg (T.W.T.); tianfook@ntu.edu.sg (T.F.K.) † These authors contributed equally to this work. * Correspondence: marcos@ntu.edu.sg Abstract: In this article, we present a microfluidic technique for the rapid enumeration of bacterial density with a syringe filter to trap bacteria and the quantification of the bacterial density through pressure difference measurement across the membrane. First, we established the baseline differential pressure and hydraulic resistance for a filtration membrane by fully wetting the filter with DI water. Subsequently, when bacteria were infused and trapped at the pores of the membrane, the differential pressure and hydraulic resistance also increased. We characterized the infusion time required for the bacterial sample to achieve a normalized hydraulic resistance of 1.5. An equivalent electric-circuit model and calibration data sets from parametric studies were used to determine the general form of a calibration curve for the prediction of the bacterial density of a bacterial sample. As a proof of concept, we demonstrated through blind tests with Escherichia coli that the device is capable of determining the bacterial density of a sample ranging from 7.3 × 10 6 to 2.2 × 10 8 CFU/mL with mean and median accuracies of 87.21% and 91.33%, respectively. The sample-to-result time is 19 min for a sample with lower detection threshold, while for higher-bacterial-density samples the measurement time is further shortened to merely 8 min. Keywords: bacterial enumeration; membrane filtration; pressure differential measurement; hydraulic resistance 1. Introduction The ability to determine the bacterial density in aqueous solution is crucial and desired in many bacteriological studies and industrial applications [1–4]. Among the methods devoted for enumerating sample bacterial density, the agar plate count is considered as the “gold standard” [5–8], in which a diluted bacterial solution is spread on an agar plate and the visible single-bacterial colonies formed after 24–48 h of incubation are counted [9–11]. However, the long incubation time and the fact that some bacteria are viable but not culturable [12,13] hinder the use of agar plate count to many experiments that require the knowledge of the density of the bacterial sample to proceed with subsequent experiments [14–16]. Furthermore, another major hurdle for agar plate count is in determining the dilution factor [17]. The number of colonies formed on the plate should ideally be between 20 and 200 colonies (or ~10 3 CFU/mL) to avoid instances where the agar plate has too few or too many colonies to count. In order to achieve a more accurate and consistent estimation, one has to take the average colony-forming unit (CFU) for 5–10 agar plates for count averaging to mitigate sampling error or inconsistency. Therefore, for a bacteria sample of unknown concentration, one has to prepare far more agar plates with multiple dilution factors. In a bid to reduce the sample-to-result time, many culture-independent techniques are developed to mitigate the need for cell culture or incubation [5,18,19]. To date, McFarland turbidity standards [20–22] and spectrophotometer measurements [23–25] are Citation: Shen, X.; Teo, T.W.; Kong, T.F.; Marcos. A Technique for Rapid Bacterial-Density Enumeration through Membrane Fitration and Differential Pressure Measurements. Micromachines 2022, 13, 1198. https://doi.org/10.3390/ mi13081198 Academic Editor: Stephen Edward Saddow Received: 13 June 2022 Accepted: 26 July 2022 Published: 28 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/license s/by/4.0/).