Acoustic Optical Fiber Sensor Based on Graphene Oxide Membrane

High-sensitivity fiber-tip acoustic sensor with ultrathin gold diaphragm

Miniature acoustic sensors with high sensitivity are highly desired for applications in medical photoacoustic imaging, acoustic communications and industrial nondestructive testing. However, conventional acoustic sensors based on piezoelectric, piezoresistive and capacitive detectors usually require a large element size on a millimeter to centimeter scale to achieve a high sensitivity, greatly limiting their spatial resolution and the application in space-confined sensing scenarios. Herein, by using single-crystal two-dimensional gold flakes (2DGFs) as the sensing diaphragm of an extrinsic Fabry-Perot interferometer on a fiber tip, we demonstrate a miniature optical acoustic sensor with high sensitivity. Benefiting from the ultrathin thickness (∼8 nm) and high reflectivity of the 2DGF, the fiber-tip acoustic sensor gives an acoustic pressure sensitivity of ∼300 mV/Pa in the frequency range from 100 Hz to 20 kHz. The noise-equivalent pressure of the fiber-tip acoustic sensor at the frequency of 13 kHz is as low as 62.8 µPa/Hz1/2, which is one or two orders of magnitude lower than that of reported optical acoustic sensors with the same size.

Deposition of Graphene Oxide on an SPR Fiber Refractometer for Sensor Applications

Graphene-based materials have been increasingly incorporated to optical fiber plasmonic sensors due to the peculiar physical and chemical properties of these materials (hardness and flexibility, high electrical and thermal conductivity, and very good adsorption for many substances, etc.). In this paper, we theoretically and experimentally showed how the addition of graphene oxide (GO) to optical fiber refractometers permits the development of surface plasmon resonance (SPR) sensors with very good characteristics. We used doubly deposited uniform-waist tapered optical fibers (DLUWTs) as supporting structures because of their already proven good performance. The presence of GO as an effective third layer is useful to tune the wavelength of the resonances. In addition, the sensitivity was improved. We depict the procedures for the production of the devices and characterize the GO+DLUWTs produced in this way. We also showed how the experimental results are in agreement with the theoretical predictions and used these to estimate the thickness of deposited GO. Finally, we compared the performance of our sensors with other ones that have been recently reported, showing that our results are among the best reported. Using GO as the medium in contact with the analyte, in addition to the good overall performance of devices, permit consideration of this option as an interesting possibility for the future development of SPR-based fiber sensors.

Graphene Oxide Thin Films for Detection and Quantification of Industrially Relevant Alcohols and Acetic Acid

Industrial environments are frequently composed of potentially toxic and hazardous compounds. Volatile organic compounds (VOCs) are one of the most concerning categories of analytes commonly existent in the indoor air of factories' facilities. The sources of VOCs in the industrial context are abundant and a vast range of human health conditions and pathologies are known to be caused by both short- and long-term exposures. Hence, accurate and rapid detection, identification, and quantification of VOCs in industrial environments are mandatory issues. This work demonstrates that graphene oxide (GO) thin films can be used to distinguish acetic acid, ethanol, isopropanol, and methanol, major analytes for the field of industrial air quality, using the electronic nose concept based on impedance spectra measurements. The data were treated by principal component analysis. The sensor consists of polyethyleneimine (PEI) and GO layer-by-layer films deposited on ceramic supports coated with gold interdigitated electrodes. The electrical characterization of this sensor in the presence of the VOCs allows the identification of acetic acid in the concentration range from 24 to 120 ppm, and of ethanol, isopropanol, and methanol in a concentration range from 18 to 90 ppm, respectively. Moreover, the results allows the quantification of acetic acid, ethanol, and isopropanol concentrations with sensitivity values of (3.03±0.12)∗104, (-1.15±0.19)∗104, and (-1.1±0.50)∗104 mL^-1, respectively. The resolution of this sensor to detect the different analytes is lower than 0.04 ppm, which means it is an interesting sensor for use as an electronic nose for the detection of VOCs.

Small-volume highly-sensitive all-optical gas sensor using non-resonant photoacoustic spectroscopy with dual silicon cantilever optical microphones

A small-volume highly-sensitive photoacoustic spectroscopy (PAS) methane detection system based on differential silicon cantilever optical microphones (SCOMs) is proposed and experimentally demonstrated. The system contains a compact non-resonant photoacoustic cell with a small volume of 1.2 mL and symmetrically-located dual SCOMs, as well as a distributed feedback laser at 1650.96 nm. The two identical SCOMs utilize the Fabry-Perot interferometric fiber-optic structure, with the differential Q-point demodulation algorithm to suppress the external vibration noise. Experimental results show that the SCOM has a high displacement sensitivity about 7.1 µm/Pa at 150 Hz and within 2.5 dB fluctuation between 5 Hz and 250 Hz. In the PAS gas sensing experiment, the normalized noise equivalent absorption coefficient of the PAS system is estimated to be 1.2 × 10^-9 cm^-1·W·Hz^-1/2 and the minimum detection limit for methane is about 111.2 ppb with 1 s integration time. External disturbance is also applied to the dual SCOM system and results show excellent stability and noise resistance. The proposed PAS system exhibits superiorities of low gas consumption, high sensitivity and immunity to vibration and electromagnetic interference, which has an enormous potential in medicine, industry and environment.

Study of Intra-Chamber Processes in Solid Rocket Motors by Fiber Optic Sensors

In this study, an experimental study of the burning rate of solid fuel in a model solid propellant rocket motor (SRM) E-5-0 was conducted using a non-invasive control method with fiber-optic sensors (FOSs). Three sensors based on the Mach–Zehnder interferometer (MZI), fixed on the SRM E-5-0, recorded the vibration signal during the entire cycle of solid fuel burning. The results showed that, when using MZI sensors, the non-invasive control of solid fuel burnout is made possible both by recording the time of arrival of the combustion front to the sensor and by analyzing the peaks on the spectrogram of the recorded FOS signal. The main mode of acoustic vibrations of the chamber of the model SRM is longitudinal, and it changes with time, depending on the chamber length. Longitudinal modes of the combustion chamber were detected by MZI only after the combustion front passed its fixing point, and the microphone was unable to register them at all. The results showed that the combustion rate was practically constant after the first second, which was confirmed by the graph of the pressure versus time at the nozzle exit.

Sound Detection Monitoring Tool in CNC Milling Sounds by K-Means Clustering Algorithm

Computer numerical control (CNC) is a machine used in the manufacturing industry to produce components quickly for the engineering field or the desired shape. In the milling process carried out by CNC machines, sometimes vibrations occur that cause unwanted cracks or damage, which if left unchecked, will cause more severe damage. For this reason, this study describes how to monitor and analyze the sound produced by CNC during the milling process. This study uses six sound sample videos from YouTube, and there are two modes: (1) the operating mode is three different shapes with XY, XZ, and XYZ axes, and the second (2) is based on material differences. Namely, wood, Styrofoam, and plastic. The sound generated from all samples of the CNC milling processes will be detected using a sound detection program that has been designed in the LabVIEW using a simple microphone. The resulting sound frequency will be analyzed using the fast Fourier transform (FFT) process in spectral measurements, which will produce the amplitude and frequency of the detected sound in real time in the form of a graph. All frequency results that have been obtained from the sound detection monitoring tool in the CNC milling machine will be imported into the K-means clustering algorithm where the different frequencies between the resonant frequency and noise will be classified. Based on the experiments conducted, the sound detection program can detect sounds with a significant level of sensitivity.