Acoustic Emission Signal of \u3cem\u3eLactococcus lactis\u3c/em\u3e before and after Inhibition with NaN\u3csub\u3e3\u3c/sub\u3e and Infection with Bacteriophage c2

The detection of acoustic emission (AE) from Lactococcus lactis, ssp lactis is reported in which emission intensities are used to follow and define metabolic activity during growth in nutrient broths. Optical density (OD) data were also acquired during L. lactis growth at 32°C and provided insight into the timing of the AE signals relative to the lag, logarithmic, and stationary growth phases of the bacteria. The inclusion of a metabolic inhibitor, NaN3, into the nutrient broth eliminated bacteria metabolic activity according to the OD data, the absence of which was confirmed using AE data acquisition. The OD and AE data were also acquired before and after the addition of Bacteriophage c2 in L. lactis containing nutrient broths during the early or middle logarithmic phase; c2 phage m.o.i. (Multiplicity of infection) was varied to help differentiate whether the detected AE was from bacteria cells during lysis or from the c2 phage during genome injection into the cells. It is proposed that AE measurements using piezoelectric sensors are sensitive enough to detect bacteria at the amount near 104 cfu/mL, to provide real time data on bacteria metabolic activity and to dynamically monitor phage infection of cells

Resonant capacitive MEMS acoustic emission transducers

Abstract. We describe resonant capacitive MEMS transducers developed for use as acoustic emission detectors, fabricated in the commercial three-layer polysilicon surface micromachining process (MUMPs). The 1-cm square device contains six independent transducers in the frequency range between 100 kHz and 500 kHz, and a seventh transducer at 1 MHz. Each transducer is a parallel plate capacitor with one plate free to vibrate, thereby causing a capacitance change which creates an output signal in the form of a current under DC bias voltage. With the geometric proportions we employed, each transducer responds with two distinct resonant frequencies. In our design the etch hole spacing was chosen to limit squeeze film damping and thereby produce an underdamped vibration when operated at atmospheric pressure. Characterization experiments obtained by capacitance and admittance measurements are presented, and transducer responses to physically simulated AE source are discussed. Finally, we report our use of the device to detect acoustic emissions associated with crack initiation and growth in weld metal

Cluster-based sensor selection framework for acoustic emission source localization in concrete

The acoustic emission (AE) method can determine the location of damage initiation and progression in large-scale structures using an array of sensors. As the location accuracy depends on proper identification of time of arrival and wave velocity, the method is more successful in homogeneous and isotropic materials than in heterogeneous materials such as concrete. The heterogeneity causes dispersive and attenuative properties such that the source-sensor distance and angle control the AE signal characteristics influencing the source location accuracy. Generally, using more than the minimum number of sensors in the location algorithm results in more accurate source localization. If the AE signal of a sensor is significantly different from the other sensors, its time of arrival may not contribute beneficially to the source localization algorithm. On the contrary, this may increase errors in determining arrival time due to signal distortion caused by dispersion, low signal-to-noise ratios resulting from attenuation, or sensors being occupied with detecting nearby noise signals. In this paper, a new cluster-based sensor selection framework is developed for selecting the best sensor combinations before applying the source localization algorithm. The framework involves selecting the best combination of sensors to input into the source localization algorithm based on the cross-correlation characteristics of signal features for single AE events or cluster analyses for large datasets. Both approaches identify the signal similarity of sensors to be used in the location sensor group. As two-dimensional source localization requires a minimum of three sensors, the number of sensor outputs extracted from the sensor selection framework is bounded by three. The source localization accuracy is evaluated using the data collected impact excitation with the known source location and structural tests of a Basalt Fiber-Reinforced Polymer (BFRP) reinforced concrete slab. The impact experiments show that the new framework to select the best three-sensor combination increases the localization accuracy to 93%, unlike the results obtained by using all six sensors (80%) and the first three sensors in the hit sequence (75%). The framework is applied to the AE data recorded from actual damage initiation and progression in a simply supported BFRP-reinforced concrete slab. The cluster of events accumulates more densely at the mid-span, where the crack initiation is validated with crack sensors and images. It is demonstrated that selecting the sensors for two-dimensional source localization using similarity analyses improves the accuracy of the source location in concrete. © 2023 Elsevier LtdThis research is based on experimental test results of a study funded by the Illinois Tollway Authority titled "Feasibility of using Basalt Fiber Reinforced Polymer (BFRP) bars as internal reinforcement in bridge decks". Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor. Sena Tayfur also acknowledges the research support of The Scientific and Technological Research Council of Turkiye (TUBITAK) through 2219 International Postdoctoral Research Fellowship Program.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTA