The availability of high-frequency, real-time measurements of the concentrations of specific metabolites in cell culture systems will enable a deeper understanding of cellular metabolism and facilitate the application of good laboratory practice standards in cell culture protocols. However, currently available approaches to this end either are constrained to single-time-point and single-parameter measurements or are limited in the range of detectable analytes. Electrochemical aptamer-based (EAB) biosensors have demonstrated utility in real-time monitoring of analytes in vivo in blood and tissues. Here, we characterize a pH-sensing capability of EAB sensors that is independent of the specific target analyte of the aptamer sequence. We applied this dual-purpose EAB to the continuous measurement of pH and phenylalanine in several in vitro cell culture settings. The miniature EAB sensor that we developed exhibits rapid response times, good stability, high repeatability, and biologically relevant sensitivity. We also developed and characterized a leak-free reference electrode that mitigates the potential cytotoxic effects of silver ions released from conventional reference electrodes. Using the resulting dual-purpose sensor, we performed hourly measurements of pH and phenylalanine concentrations in the medium superfusing cultured epithelial tumor cell lines (A549, MDA-MB-23) and a human fibroblast cell line (MRC-5) for periods of up to 72 h. Our scalable technology may be multiplexed for high-throughput monitoring of pH and multiple analytes in support of the broad metabolic qualification of microphysiological systems.

Developing biosensors for real-time monitoring is crucial for understanding cell metabolism and improving quality control in biotechnology. This article presents a biocompatible, high frequency scanned, dual-purpose electrochemical aptamer based (EAB) sensor for real-time monitoring of pH and amino acid (phenylalanine) concentration. The sensor can measure phenylalanine concentrations ranging from 1 mu M to 3 mM and pH. Additionally, a leakless reference electrode is integrated into the system to prevent Ag+ ion cytotoxicity in cell cultures, enhancing the sensor's biocompatibility. This sensor allows researchers to gain deeper insights into cellular pH and amino acid consumption, advancing fundamental research in biology and biotechnology.

Multispectral imaging involves capturing the same scene at different wavelengths using various narrowband filters stacked or integrated into digital camera sensors. This technology makes it possible to extract the additional information that a human eye or conventional camera fails to capture and thus has important applications in object identification, precision agriculture, and medicine. Multispectral imaging in visible wavelengths is readily possible due to the availability of digital imaging sensors and existing narrowband filter designs like metal-dielectric-metal films, dielectric films, or Fabry-Perot cavities [1-2]. Multispectral imaging in thermal longwave infrared (LWIR) wavelengths of 8-14 mu m range has more advanced applications as they can see through fire, detect various gases, and investigate materials non-destructively through thermal signatures. However, conventional thermal image sensors can image in a single spectral band only. Thermal multispectral imaging is hindered by traditional filter technology where many layers of different materials are required for obtaining various spectral bands and limited wavelength tunability. On-chip integration of the infrared filters on the thermal image sensors to build a compact multispectral thermal camera is still an emerging area [3-5]. In the current work, we design and demonstrate a low-cost single sensor-based multispectral thermal sensor system composed of copper-based plasmonic imaging filter mosaic (multiple spectral filters are fabricated on a single substrate using only one lithography step and two deposition steps) integrated into an uncooled monochrome thermal sensor. The proposed work is mass-fabricable, scalable, and integrable, thereby leveraging next-generation LWIR thermal snapshot multi- and hyperspectral imaging.

A multispectral camera records image data in various wavelengths across the electromagnetic spectrum to acquire additional information that a conventional camera fails to capture. With the advent of high-resolution image sensors and color filter technologies, multispectral imagers in the visible wavelengths have become popular with increasing commercial viability in the last decade. However, multispectral imaging in longwave infrared (LWIR, 8–14 μm) is still an emerging area due to the limited availability of optical materials, filter technologies, and high-resolution sensors. Images from LWIR multispectral cameras can capture emission spectra of objects to extract additional information that a human eye fails to capture and thus have important applications in precision agriculture, forestry, medicine, and object identification. In this work, we experimentally demonstrate an LWIR multispectral image sensor with three wavelength bands using optical elements made of an aluminum (Al)-based plasmonic filter array sandwiched in germanium (Ge). To realize the multispectral sensor, the filter arrays are then integrated into a three-dimensional (3D) printed wheel stacked on a low-resolution monochrome thermal sensor. Our prototype device is calibrated using a blackbody and its thermal output has been enhanced with computer vision methods. By applying a state-of-the-art deep learning method, we have also reconstructed multispectral images to a better spatial resolution. Scientifically, our work demonstrates a versatile spectral thermography technique for detecting target signatures in the LWIR range and other advanced spectral analyses.

Wireless endovascular sensors and stimulators are emerging biomedical technologies for applications such as endovascular pressure monitoring, hyperthermia, and neural stimulations. Recently, coil-shaped stents have been proposed for inductive power transfer to endovascular devices using the stent as a receiver. However, less work has been done on the external transmitter components, so the maximum power transferable remains unknown. In this work, we design and evaluate a wearable transmitter coil that allows 50 mW power transfer in simulation.Clinical Relevance—This allows more accurate measurements and precise control of endovascular devices.

Spectral imaging allows data acquisition at any particular frequency range in the electromagnetic (EM) spectrum to extract additional information. The light energy emitted from the sources or reflected by the objects is selectively sensed in specific bands of the EM spectrum to produce images. Multispectral imaging in long-wave infrared (thermal wavelengths) is an emerging research area due to its ability to identify objects precisely from the emission spectrum for chemical detection, gas sensing, night vision, and surveillance applications.1 The multispectral imaging in the long-wave infrared is limited by the conventional filter technologies and limited materials responding in the thermal wavelengths. Wavelength filters based on surface plasmon resonance can overcome such limitations and can produce filters using a single nanoscale thick metal film with wide wavelength tunability. This paper presents a wavelength filter technology operating in six bands of the thermal region (7 – 14 μm). With copper (Cu) as the metal layer and germanium (Ge) as the cap layer, the transmission efficiency of lithographically patterned multilayer plasmonic filters on gallium arsenide (GaAs) substrate has improved up to 60% in thermal wavelengths. Thus, a thermal multispectral filter system is realized to acquire narrow transmission bands in the thermal region using surface infrared plasmonics of Cu in a dielectric-metal-dielectric (DMD) mosaic of GaAs-Cu-Ge. The filters are then integrated on thermal image sensors mounted on an in-house processing electronic platform to develop a six-band multispectral thermal sensor system. The working of the snapshot thermal multispectral sensor system is demonstrated by capturing the images at six different bands. The developed multispectral system can be adopted for non-destructive thermal imaging or can be used directly as a microspectrometer for various thermal spectroscopy applications

Hyperspectral camera system captures information using large number of wavelength bands with narrow spectral width in contrast to multispectral camera with a few bands across the electromagnetic spectrum. Hyperspectral data cube can provide significant amount of information in target detection. However, such systems are bulky and generate enormous amount of data and hence the real time processing is challenging for light weight airborne platform and wearable sensor system development. With recent advancement in CMOS image sensor and colour filter technologies, multispectral camera system has become compact for the lightweight applications. This paper demonstrates the suitability of a few selected bands from the multispectral camera combined with signature based machine learning techniques can provide accurate target detection. The study has used a four-band multispectral and one hundred and thirty eight bands hyperspectral systems mounted on a drone platform to detect a camouflage sheet of size 250cm x 65cm from different heights. The results will have application in the development of compact spectral image sensor technology suitable for aerial and hand held, or helmet/body mounted applications.

The assessment of aromas in beer is critical to assess its quality since it could be used as an indicator of contamination or faults, which will directly influence consumers' acceptability. Traditional techniques to evaluate aromas are time-consuming, require special training, costly equipment, and trained personnel. Therefore, this study aimed to develop a portable, low-cost electronic nose (e-nose) coupled with machine learning modeling to predict aromas in beer. Nine different gas sensors were used i) ethanol, ii) methane, iii) carbon monoxide, iv) hydrogen, v) ammonia/alcohol/benzene, vi) hydrogen sulfide, vii) ammonia, viii) benzene/alcohol/ammonia and ix) carbon dioxide. Output data were assessed for significant differences using ANOVA and least significant differences as post hoc test (alpha = 0.05). Two artificial neural network (ANN) models were also developed to predict i) the peak area of 17 different volatile aromatic compounds (Model 1) obtained from gas chromatography-mass spectroscopy (GC-MS) and ii) the intensity of ten sensory descriptors acquired from a sensory session with 12 trained panelists. Results from the ANOVA showed that there were significant differences between the samples used, which showed that the e-nose was able to discriminate samples. The resulting ANN models were highly accurate with correlation coefficients of R = 0.97 (Model 1) and R = 0.93 (Model 2). The combined method using the developed e-nose and the ANN models could be used by the industry as a low-cost, rapid, reliable and effective technique for beer quality assessment within the production line. This may also be calibrated for its use in other foods and beverages.