Spectroscopy Sensor Research Articles

Gold-coated Impedance Spectroscopy Sensors on PCB and PET for Real-Time Monitoring of Cancer Cells

Abstract Electrical cell-substrate impedance sensing (ECIS) biosensors are widely used for in vitro cancer cell monitoring as they are label-free, require small sample volumes, and allow real-time monitoring. ECIS electrodes are typically made of pure gold, but the usage of pure gold electrodes is too costly for single-use applications. As an alternative, this work proposes the use of gold coatings on a printed sensor's electrodes. The interdigitated electrode design was used on glass fiber-reinforced epoxy resin for printed circuit boards (PCB), and polyethylene terephthalate (PET). The Cu/Ni electrodes on PCB were electroplated with Au, while the Cu/Ni electrodes on PET were coated with Au using an electroless technique. The physicochemical properties were studied using optical microscopy, scanning electron microscopy, and energy-dispersive spectroscopy. Electrochemical characterization was done using cyclic voltammetry and electrochemical impedance spectroscopy. Biocompatibility assessment and sensor functionality tests were done by culturing SiHa cervical cancer cell lines on these sensors and impedance measurements. The results show that both electroplated and electroless sensors were biocompatible and suitable to monitor SiHa cell growth. Electrochemical migration effect was observed on the sensors where the reaction occurred at 1.2V DC for the PCB sensor and 1.0V DC for the PET sensor.

Multimodal In-Sensor Computing System Using Integrated Silicon Photonic Convolutional Processor.

Photonic integrated circuits offer miniaturized solutions for multimodal spectroscopic sensory systems by leveraging the simultaneous interaction of light with temperature, chemicals, and biomolecules, among others. The multimodal spectroscopic sensory data is complex and has huge data volume with high redundancy, thus requiring high communication bandwidth associated with high communication power consumption to transfer the sensory data. To circumvent this high communication cost, the photonic sensor and processor are brought into intimacy and propose a photonic multimodal in-sensor computing system using an integrated silicon photonic convolutional processor. A microring resonator crossbar array is used as the photonic processor to implement convolutional operation with 5-bit accuracy, validated through image edge detection tasks. Further integrating the processor with a photonic spectroscopic sensor, the in situ processing of multimodal spectroscopic sensory data is demonstrated, achieving the classification of protein species of different types and concentrations at various temperatures. A classification accuracy of 97.58% across 45 different classes is achieved. The multimodal in-sensor computing system demonstrates the feasibility of integrating photonic processors and photonic sensors to enhance the data processing capability of photonic devices at the edge.

Developing A Digital Twin of a TRUEX Extraction Process that Enables Non-proliferation and Safeguards Monitoring through Optical Spectroscopy and Machine Learning

Background The recycling of spent fuel can increase the longevity and decrease the waste footprint associated with the nuclear fuel cycle. However, nuclear fuel reprocessing introduces the risk of proliferation of fissile material. For example, the recycling of uranium (U) and plutonium (Pu) is generally accomplished via a solvent extraction process. The separation efficiency of the process is dependent on a variety of parameters such as acid concentration, organic to aqueous (O/A) volume ratio, number of separation stages, etc. A change in any of these parameters could result in different extraction conditions of fissile material and lead to a process upset with potential safeguards and proliferation concerns. Thus, the ability to monitor the solvent extraction process in real-time would enable the rapid identification and diagnosis of process upsets. Methods In this work, we discuss the development of a digital twin of a surrogate small-scale nuclear fuel reprocessing solvent extraction process utilizing online optical spectroscopic monitoring, machine learning (ML), and chemical modeling. A cascade of centrifugal contactors was employed with the TRUEX (TransUranic Extraction) solvent and surrogate minor actinides. Spectroscopic sensors were placed at strategic locations within the contactor bank and were used to predict TRUEX surrogate analyte concentrations at those locations. Results A solvent extraction digital twin consisting of the data acquisition, chemical model, and anomaly detection, provided expected concentrations at each contactor during normal operations. The digital twin demonstrated the ability to accurately simulate the extraction behavior under ideal conditions. Furthermore, the digital twin’s ML anomaly detection algorithm was shown to be successful in identifying several non-ideal process upset conditions.

Muscle Oxygen Saturation Dynamics During Upper-Body Resistance Exercise

Research examining the changes in muscle oxygen saturation across multiple sets of resistance exercise is limited. The purpose of this study was to describe the physiological response of muscle oxygenation parameters during upper-body resistance exercise and examine the differential effects of relevant participant characteristics on resistance training performance and muscle oxygen saturation dynamics. Sixty-one recreationally trained men (n = 44; 21.8 ± 2.6 years) and women (n = 17; 20.2 ± 1.8 years) completed five-repetition maximum sets of barbell bench presses at a load equal to 75% 1-RM with a 2 min rest interval. Muscle oxygen saturation (SmO2) dynamics within the anterior deltoid were monitored using a portable near-infrared spectroscopy sensor. The percent change in SmO2 (∆%SmO2), the muscle oxygen re-saturation rate (SmO2RecSlope), and the highest measured SmO2 value during recovery periods (SmO2Peak) were measured. Two-way (sex [men, women] x time [sets 1–5]) repeated measures analyses of variance (ANOVA) were performed on muscle saturation variables. To examine the effect of relevant controlling variables, separate analyses of covariance (ANCOVA) with repeated measures were also performed. No differences were seen with ∆%SmO2 across sets. The main effects for sets occurred for SmO2RecSlope, whereby a decline was noted on sets 4 and 5 (p = 0.001) compared to set 1. Additionally, SmO2Peak was the lowest on set 5 (p < 0.001) compared to all other sets. Moreover, body mass (p = 0.013), diastolic blood pressure (p = 0.044), and mean arterial pressure (p = 0.033) for ∆%SmO2 were the only significant covariates noted amongst the muscle oxygenation variables. In conclusion, no sex differences and only a few set differences in muscle oxygen saturation dynamics were seen without employing any covariates. Body mass, diastolic blood pressure, and mean arterial pressure were identified as factors that could influence observed responses.

Metal–Organic Framework-Ag Nanocomposites as Photocatalysts and in Situ Surface-Enhanced Raman Spectroscopy Sensors to Study the Mechanisms of CO2 Reduction Reactions

Metal–Organic Framework-Ag Nanocomposites as Photocatalysts and in Situ Surface-Enhanced Raman Spectroscopy Sensors to Study the Mechanisms of CO₂ Reduction Reactions

Electric Field-Induced Enhanced Raman Spectroscopy Sensor and Photocatalysis with Thermoelectric-Plasmonic Metal Nanocomposites.

Electric field-induced surface-enhanced Raman scattering (E-SERS) substrates have been proven to further enhance the attained Raman intensity. Herein, integrated with plasmonic Ag nanoparticles (Ag NPs), the thermoelectric Bi2Te3 plate as an E-SERS substrate decreased the limit of detection by 2 orders of magnitude and increased the SERS signal by >20 times compared to those without electrical field induction. The thermoelectric potential produced by the Bi2Te3 plate could modulate the electron density and subsequently change the Fermi level of Ag. This increases the resonant electron transition probability using a broad range of molecules. The plasmon-activated catalytic reactions of the interconversion between p-nitrothiophenol and p,p'-dimercaptoazobenzene could be controlled through the E-SERS template. On the basis of the finite element method, explicit theoretical analysis indicated that the Ag NP-Bi2Te3-molecule charge transfer could improve our understanding of the SERS and photocatalytic mechanism.

All Fiber-Optic Photoacoustic Spectroscopy Sensor Combining Resonance and Nonresonance Photoacoustic Cells for High-Sensitivity Simultaneous Dual-Gas Sensing

All Fiber-Optic Photoacoustic Spectroscopy Sensor Combining Resonance and Nonresonance Photoacoustic Cells for High-Sensitivity Simultaneous Dual-Gas Sensing

Molecularly Imprinted Polymers Electrochemical Sensing: The Effect of Inhomogeneous Binding Sites on the Measurements. A Comparison between Imprinted Polyaniline versus nanoMIP-Doped Polyaniline Electrodes for the EIS Detection of 17β-Estradiol.

Molecularly imprinted polymers (MIPs) are synthetic receptors made by template-assisted synthesis. MIPs might be ideal receptors for sensing devices, given the possibility to custom-design selectivity and affinity toward a targeted analyte and their robustness and ability to withstand harsh conditions. However, the synthesis of MIP is an inherently random process that produces a statistical distribution of binding sites, characterized by a variety of affinities. This is verified both for bulk MIP materials and for MIP's thin layers. In the present work, we aimed at assessing the effects of inhomogeneous versus homogeneous imprinted binding sites on electrochemical sensing measurements, and the possible implications on the sensor's performance. In the example of an Electrochemical Impedance Spectroscopy (EIS) sensor for the 17β-estradiol (E2) hormone, the scenario of inhomogeneous binding sites was studied by modifying electrodes with an E2-MIP polyaniline (PANI) thin layer, called the "Imprinted PANI layer". In contrast, the condition of discrete and uniform binding sites was epitomized by electrodes modified with a thin PANI layer purposedly doped with E2-MIP nanoparticles (nanoMIPs), which were referred to as "nanoMIP-doped PANI". The behaviors of the two EIS sensors were compared. Interestingly, the sensitivity of the nanoMIP-doped PANI was almost twice with respect to that of the imprinted PANI layer, strongly suggesting that the homogeneity of the binding sites has a fundamental role in the sensor's development. The nanoMIP-doped PANI sensor, which showed a response for E2 in the range 36.7 pM-36.7 nM and had a limit of detection of 2.86 pg/mL, was used to determine E2 in wastewater.

A Fiber optics based surface enhanced Raman spectroscopy sensor for chemical and biological sensing

This paper investigates an innovative surface-enhanced Raman scattering (SERS) sensor developed on a side-polished multimode optical fiber core. The optical fiber was integrated into specifically designed 3-dimensional printed mold, where manual polishing of the fiber took place. Microsphere Photolithography (MPL) techniques was employed to pattern periodic nanoantenna arrays on the polished surface, incorporating multiple disk diameters at a fixed periodicity. Subsequent gold deposition/lift-off were carried out to transfer the pattern from the photoresist to the fiber core, resulting in highly periodic hexagonal closed pack (HCP) arrays of nanodisks. These arrays can significantly enhance the SERS signal intensity compared to that of the fiber tip. The sensor's performance was demonstrated using various concentrations of Rhodamine 6G (R6G) dye ranging from 10−5 to 10−9 M as a function of disk diameter and sensing surface area. The resulting spectra revealed characteristic peak positions that aligned well with the fingerprint Raman spectra of R6G. The results demonstrates that the sensitivity is 10−9 M for the sensor with an 800 nm disk diameter.

Low-cost signal enhanced colorimetric and SERS dual-mode paper sensor for rapid and ultrasensitive screening of mercury ions in tea

Mercury ions (Hg2+) are highly toxic heavy metals that are commonly found in natural environments. Owning to their non-biodegradability and accumulation in the food chain, the precise detection of trace amounts of Hg2+ is essential for preventing chronic accumulation and ensuring food safety. In this study, we present a dual-mode paper sensor for simultaneous colorimetric and Surface-Enhanced Raman Spectroscopy (SERS) detection of Hg2+ in tea, achieving ultrasensitive, rapid, and on-site screening. 4-Mercaptopyridine (4-MPY) was effectively chemisorbed onto the gold nanoparticles (AuNPs), acting as a signal probe for colorimetric methods. Moreover, it can produce plasmonic hot spots for SERS by interacting with the pyridine ring. To enhance the signal intensity of both colorimetry and SERS, a silver shell is in-situ grown on the surface of AuNPs captured on the paper sensor by reduction of Ag+, achieving signal amplification. The visual limit of detection (LOD) for the colorimetric biosensor is 2.5 pM, while the LOD of SERS is 0.48 pM with this dual-mode paper sensor. The sensitivity of both the colorimetric method and SERS was improved by approximately 200 and 500 times, respectively, with the designed signal amplification strategy. The system allows for multiple parallel screening of the same sample, ensuring accurate results without any false-positive or false-negative. This study provides a valuable platform for the accurate detection of various other heavy metal ions and provides effective strategies for improving the performance of colorimetric methods.

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring

Creation of multimodal digital twins with reflexive AGI multilogic and multisensory

Reflexive AGI multilogic, multimodality and multisensors are the basis for the multidisciplinary development of intelligent digital twins. Multimodality is implemented in several formats: text, image, speech, formulas, etc. Multilogic is implemented according to several rules or methods or ways of working with knowledge and data, and the criterion for selecting the best result from all implementations. AGI multilogic carries out judgment, understanding, perception, comparison, analysis, choice, etc. Understanding is realized using the technology of unified objectification of the ontology of subject areas for the implementation of specific activities. Multisensor systems are combined groups of electro-optical, spectroscopic and holographic sensors, and combined series of sensors, such as a thermal imager, color camera, low-light camera, laser rangefinder, laser designator, laser, pointer-illuminator and others. Multisensory systems help monitor the psyche and performance of a person at the level of medical indicators of his physical condition in the process of joint activities with digital twins. Multimodal digital twin with AGI multilogic and multisensory is good human assistant in many areas of activity.

Part-per-Billion (Ppb)-Level Acetylene Sensor Employing Optical Quartz Enhanced Photoacoustic Spectroscopy (QEPAS) with an Erbium-Doped Fiber Amplifier (EDFA) and a Fiber-Optic Fabry–Perot Interferometer

An all-optical double-pass quartz enhanced photoacoustic spectroscopy (QEPAS) sensor for the detection of acetylene (C2H2) at part-per-billion (ppb) levels was developed and experimentally validated. By employing an erbium-doped fiber amplifier (EDFA) capable of emitting up to 1 W of optical power, a commercial quartz tuning fork (QTF) resonating at 30.7 kHz, and double-pass acoustic microresonators, the amplitude of the QEPAS signal was significantly enhanced, thereby improving the sensitivity of the C2H2-QEPAS sensor. Instead of using piezoelectric detection in conventional QEPAS, a highly sensitive fiber-optic Fabry–Perot interferometer (FPI) was employed to measure the vibration of the QTF prong. A working point self-stabilizing technique was exploited to improve the demodulation sensitivity and stability. The linear response of the sensor to laser power and C2H2 concentration confirmed that no saturation occurred. With an excitation laser power of 1 W and a C2H2 absorption line of 1532.83 nm, a C2H2 minimum detection limit (MDL) of 19.1 ppb was achieved, corresponding to a normalized noise equivalent absorption coefficient (NNEA) of 1.58 × 10−8 cm−1∙W∙Hz−1/2. Owing to its high sensitivity, long-term stability, and immunity to electromagnetic interferences, the QEPAS sensor provides considerable potential for remote gas sensing in environmental monitoring and other applications.

Simultaneous Detection of Major Greenhouse Gases with Multiresonator Photoacoustic Spectroscopy.

Greenhouse gas (GHG) detection plays an important role in climate change research and industry applications. A novel photoacoustic spectroscopy (PAS) sensor based on multiple resonators has been developed for the detection of GHGs. The major GHGs CO2, CH4, and N2O were measured simultaneously using only one acoustic sensor by coupling three acoustic resonators into a photoacoustic cell. A sinusoidal voltage signal-driven noise source was integrated into a multiresonator photoacoustic cell, allowing convenient calibration of the resonant frequency of the photoacoustic cell. The performance of the sensor was further enhanced by reflecting a laser beam four times in the photoacoustic cell. Allan deviation analysis showed that the minimum detection limits of 2.7 ppm, 90 ppb, and 1 ppb could be achieved for CO2, CH4, and N2O, respectively, over a 300 s integration time. The feasibility of the system was confirmed by continuous measurements of the three major GHGs from different sources for up to 10 h.

Theoretical Aspects of Creating Multimodal Digital Twins with AGI Multilogic and Multisensory

AGI multilogic, multimodality and multisensors are the basis for the multidisciplinary development of intelligent digital twins. Multimodality is implemented in several formats: text, image, speech, formulas, etc. Multilogic is implemented according to several rules or methods or ways of working with knowledge and data, and the criterion for selecting the best result from all implementations. AGI multilogic carries out judgment, understanding, perception, comparison, analysis, choice, etc. Understanding is realized using the technology of unified objectification of the ontology of subject areas for the implementation of specific activities. Multisensor systems are combined groups of electro-optical, spectroscopic and holographic sensors, and combined series of sensors, such as a thermal imager, color camera, low-light camera, laser rangefinder, laser designator, laser, pointer-illuminator and others. Multisensory systems help monitor the psyche and performance of a person at the level of medical indicators of his physical condition in the process of joint activities with digital twins. Multimodal digital twin with AGI multilogic and multisensory is good human assistant in many areas of activity.

Electrochemical potential enhanced EC-SERS sensor for sensitive and label-free detection of acetamiprid

Label-free surface-enhanced Raman spectroscopy (SERS) holds promise for detecting pesticide residues, yet its broader application in food safety is limited by the weak affinity between pesticides and SERS substrates. This study introduces an electrochemical surface-enhanced Raman spectroscopy (EC-SERS) sensor that utilizes potential strengthened molecular interactions and Ag@SiO2 nanospheres as SERS substrates, significantly enhancing the detection sensitivity for acetamiprid (AAP). The dense distribution of silver nanoparticles (Ag NPs) on the SiO2 surfaces creates numerous “hot spots,” significantly improving the SERS performance for AAP detection. A potential of −0.5 V substantially boosts the SERS signal intensity for AAP compared to without applied potential, notably achieving a 4.3-fold increase at the 631 cm−1 signal peak. Under optimal conditions, the EC-SERS method achieved a limit of detection (LOD) for AAP at 0.046 μM, spanning a linear range from 0.05 μM to 0.1 mM, which is 185 times more sensitive than conventional SERS approaches. When applied to vegetable samples, the method showed recoveries between 95.56 % and 109.33 %, with results corroborated by HPLC-MS analysis. Thus, this study provides an effective and facile strategy for the detection of AAP in the food safety field.

A Deep Learning Approach to Investigating Clandestine Laboratories Using a GC-QEPAS Sensor

Illicit drug production in clandestine laboratories involves the use of large quantities of different chemicals that can be obtained for legitimate purposes. The identification of these chemicals, including reagents, catalyzers and solvents, is crucial for forensic investigations. From a legal point of view, a drug precursor is a material that is specific and critical to the production of a finished chemical and that constitutes a significant portion of the final molecular structure of the drug. In this study, a gas chromatography quartz-enhanced photoacoustic spectroscopy (GC-QEPAS) sensor—in conjunction with a deep learning model—was evaluated for its effectiveness in the detection and identification of interesting compounds for the production of amphetamine, methamphetamine, 3,4-methylenedioxymethamphetamine (MDMA), phenylcyclohexyl piperidine (PCP), and cocaine. The GC-QEPAS sensor includes a gas sampler, a fast GC for separation, and a QEPAS detector, which excites molecules exiting the GC column using a quantum cascade laser to provide the infra-red (IR) spectrum. The on-site capability of the GC-QEPAS system offers significant advantages over the current instruments employed in this field, including rapid analysis, which is crucial in field operations. This allows law enforcement to quickly identify specimens of interest on site. The system’s performance was validated by taking into account the limit of detection, repeatability, and within-laboratory reproducibility. The results showed excellent repeatability and reproducibility for both the GC and QEPAS modules. The deep learning model, a multilayer perceptron neural network, was trained using IR spectra and retention times, achieving very high classification accuracy in the testing conditions. This study demonstrated the efficacy of the GC-QEPAS sensor combined with a deep learning model for the reliable identification of drug precursors, providing a robust tool for law enforcement during criminal investigations in clandestine laboratories.

New Approach for Plasma Nitrocarburizing of Stainless Steels by a Modified Reactor Configuration Using a Plasma‐Activated Solid Carbon Precursor

Stainless steel surfaces can be modified using plasma‐assisted thermochemical treatments to improve properties like hardness, wear, and pitting corrosion resistance. To specifically adjust the desired properties, a precise control of the produced treatment‐relevant gas species with regard to their type and concentration is essential. This requires the adjustment of the parameters for the generation of the gas species, being independent from heating parameters, as well as their real‐time measurement. Therefore, this study presents the use of a plasma‐activated solid carbon precursor in a cold‐wall reactor using active screen technology and in a modified hot‐wall reactor during plasma nitrocarburizing of austenitic stainless steel. In addition, the modified hot‐wall reactor combined with a compact laser‐based absorption spectroscopy sensor for real‐time monitoring and concentration evaluation of in‐site generated gas species. It is shown that implementing a plasma‐activated solid carbon precursor in a modified hot‐wall reactor enables adjustable generation of C‐containing gas species, particularly HCN, with high production yield by an independent power management. Therefore, HCN is produced independent from heating while the limitations arising during active screen technology using a carbon screen are avoided. The presented technological development thus opens up new possibilities for better control of the plasma nitrocarburizing treatments of steels.

Segregation of Municipal Solid Waste Based on Their Biodegradability Using Spectroscopy Sensor

Segregation of Municipal Solid Waste Based on Their Biodegradability Using Spectroscopy Sensor

Nitric Oxide and Temperature Measurement Using Laser Absorption Spectroscopy in Arc-Heater Plenum

A mid-infrared tunable diode laser absorption spectroscopy (TDLAS) sensor to measure rovibrational temperature and nitric oxide (NO) mole fraction in the arc-heater plenum was developed and employed in the Arc Heated Combustion Tunnel-II (ACT-II) facility at the University of Illinois at Urbana-Champaign. From this study, TDLAS-inferred temperatures were found to differ from the conventional method to estimate temperature by over 13%. Chemical equilibrium simulations were performed at the TDLAS-inferred temperatures to understand how chemical relaxation timescales compare with plenum residence times. Results from this study show NO levels higher than equilibrium concentrations, with relaxation timescales that exceed plenum residence times. With insufficient time to reach equilibrium, elevated NO levels are expected to remain in the test gas and persist into the scramjet model.