In-line Detection Research Articles

In-line gas purging exploiting a novel converging flow-on paper optosensing device for the determination of total ammonia-nitrogen in water samples

A novel, inexpensive, and sensitive microanalytical system for the determination of total ammonia-nitrogen (ammonia-N) in water was developed. The system was designed to detect ammonia using an in-line purging system coupled with a newly designed gas converging flow cell incorporating in-line paper-based colorimetric detection. The gas converging flow cell served as a gas chamber, a paper-based device holder, and a color detection cell. The paper-based device was impregnated with butterfly pea extract (a natural acid-base indicator), the color of which changed on the basis of the generated ammonia gas. Analytical parameters such as concentration of natural indicator and purging time were studied and optimized. The color image could be captured by an integrated endoscope camera or smartphone and analyzed by ImageJ software in RGB mode. Under optimal conditions, two linear dynamic ranges were observed. By using 1:2 (v/v) diluted 2% (w/v) butterfly pea extract in Milli-Q water, a low linear concentration range of 0.05–0.9 mg L−1 with a limit of detection (LOD) of 0.02 mg L−1 and limit of quantification (LOQ) of 0.05 mg L−1 was achieved. By using a non-diluted 2% (w/v) butterfly pea extract, a high linear concentration range of 0.5–4 mg L−1 with an LOD of 0.1 mg L−1 and LOQ of 0.3 mg L−1 was obtained. Finally, the optimized method was leveraged to determine ammonia-N in various water samples from shrimp farming.

Deep learning-based domain adaptation for a generalized detection of wear phenomena during blanking

Blanking plays an important role in most manufacturing chains as this forming operation defines the final geometry as well as functional properties of the product. As a result of high production rates combined with excessive mechanical and dynamic loads, wear is an unavoidable phenomenon in such processes and directly worsens their economic efficiency. Although wear plays this crucial role, its detection is mainly limited to the manual control or inline binary fault detection. In this context, data-driven approaches such as conventional machine or deep learning allow to overcome this challenge by an inline wear state estimation. Unfortunately, their application in practice is limited since changes to the system configuration significantly degrade the performance of the implemented data-driven model. To address this problem, an adapted deep learning-based domain adaptation (DA) technique is proposed in this paper. With this new procedure, sensorial acquired force singles are combined with a deep network architecture to enable an inline detection of finely graded wear states in a blanking process, even if there has been a change in system configuration. As a result, high accuracies for classifying the 11 wear states are achieved without retraining the underlying models, in case of a distribution discrepancy between source and target domain. In contrast to no DA technique as well as a conventional DA technique applied to transfer tasks, the proposed method improves the classification accuracy in the target domain up to 95%. Validation of this approach based on different real-world data sets demonstrates its suitability for a generalized inline wear state estimation, even under varying process boundaries.

Three-core photonic crystal fiber for the in-line measurement of full-polarization states by multi-core polarization interference theory.

In this Letter, we demonstrate and experimentally verify the application of three-core photonic crystal fiber (3C-PCF) for the in-line detection of fully polarized states. We prove the response of 3C-PCF to full-polarization states under multi-core polarization interference through experiments. The sensitivity at 1472 nm is 0.0273 nm/rad, and the linear response is better than 98.9% (the optimal operating wavelength can be designed in the range of 1470 to 1570 nm). With the advantages of an all-fiber integrated system, robustness, and wide wavelength coverage, our design holds great promise for facilitating fiber-optic-integrated polarization meters for optical fiber communication and biomedical diagnostic applications.

Lightweight Gas Sensor Based on MEMS Pre-Concentration and Infrared Absorption Spectroscopy Inside a Hollow Fiber

This paper reports on a compact and lightweight sensor for analysis of gases/vapors by means of a MEMS-based pre-concentrator coupled to a miniaturized infrared absorption spectroscopy (IRAS) module. The pre-concentrator was utilized to sample and trap vapors in a MEMS cartridge filled with sorbent material and to release them once concentrated by fast thermal desorption. It was also equipped with a photoionization detector for in-line detection and monitoring of the sampled concentration. The vapors released by the MEMS pre-concentrator are injected into a hollow fiber, which acts as the analysis cell of the IRAS module. The miniaturized internal volume of the hollow fiber of about 20 microliters keeps the vapors concentrated for analysis, thus allowing measurement of their infrared absorption spectrum with a signal to noise ratio high enough to identify the molecule, despite the short optical path, starting from sampled concentration in air down to parts per million. Results obtained for ammonia, sulfur hexafluoride, ethanol and isopropanol are reported to illustrate the sensor detection and identification capability. A limit of identification (LoI) of about 10 parts per million was validated in the lab for ammonia. The lightweight and low power consumption design of the sensor allowed operation onboard unmanned aerial vehicles (UAVs). The first prototype was developed within the EU Horizon 2020 project ROCSAFE for the remote assessment and forensic examination of a scene in the aftermath of industrial or terroristic accidents.

Towards deep computer vision for in-line defect detection in polymer electrolyte membrane fuel cell materials

Polymer Electrolyte Membrane (PEM) fuel cells are a promising source of alternative energy. However, their production is limited by a lack of well-established methods for quality control of their constituent materials like the membrane-electrode assembly during roll-to-roll manufacturing. One potential solution is the implementation of deep learning methods to detect unwanted defects through their detection in scanned images. We explore the detection of defects like scratches, pinholes, and scuffs in a sample dataset of PEM optical images using two deep learning algorithms: Patch Distribution Modeling (PaDiM) for unsupervised anomaly detection and Faster-RCNN for supervised object detection. Both methods achieve scores on performance metrics (ROC-AUC and PRO-AUC for PaDiM and AP for Faster-RCNN) that are comparable to their scores on benchmark datasets. These methods also have the potential to detect a wider range of defects compared to IR thermography and previous optical inspection methods. Overall, deep learning shows promise at detecting relevant defects of interest and has the potential to achieve real-time defect detection.

Coating Defects of Lithium-Ion Battery Electrodes and Their Inline Detection and Tracking

In order to reduce the cost of lithium-ion batteries, production scrap has to be minimized. The reliable detection of electrode defects allows for a quality control and fast operator reaction in ideal closed control loops and a well-founded decision regarding whether a piece of electrode is scrap. A widely used inline system for defect detection is an optical detection system based on line scan cameras and specialized lighting. The cameras scan the electrode, and brightness differences on the surface are detected and processed inline. The characteristics of the defect image are used for automated classification of the defects based on image features. Furthermore, the detailed detection of defects allows for the identification of causes. This paper describes the working principle of such an inline detection system, the catalog of typical defects, and the image features used to classify them automatically. Furthermore, we propose and discuss causes and effects of the different defect types on the basis of the literature and expert experience. In combination with tracking and tracing, this enables the manufacturer to reduce scrap by detecting defects early in the production chain.

Fully automated microfluidic mixing aided in-line detection of trace level oil field tracers

Recent development in oil field tracer technologies has been focused on implementations that increase oil recovery through optimized algorithms for production, aided by the addition of high fidelity interwell tracer data that improve reservoir history match. Prerequisites for successful tracer campaigns in oil reservoirs at a large scale not only include low-cost tracer materials with a rich palette of uniquely identifiable tracers, but also high sensitivity, easy-to-use trace-level detection and analysis methodologies. A fully automated in-line separation and detection protocol can simultaneously address challenges in throughput (speed and efficiencies) and accuracy (minimization of error-prone steps) of multi-tracer detection. Our previously reported tracer materials consist of barcoded variants of the dipicolinic acid (DPA) molecules that sensitize the emission of lanthanide ions. We have capitalized on the time-gated optical luminescence detectability of this sensitizer-lanthanide ion complex to reject background oil fluorescence, achieving high detection sensitivity in particularly complex oil field produced brine matrix. However, automation of the detection and analysis process has been hindered by opposing requirements in the integration of chromatography instrumentation (separation resolution) with in-line mixing (dispersion due to increase in residence time), and high sensitivity spectrofluorimetry (long integration time). We overcome this major barrier to implementing an in-line detection design under flow conditions by utilizing a microfluidic mixing chip which offers a high degree of mixing in a very short fluidic path length for the in-flow chelation of lanthanide ion to the chromatographically resolved tracer variants, and a flow-cell fluorescence detector at the output of the high-performance liquid chromatography (HPLC). In this study, we demonstrated the rapid in-line detection of a mixture of three different DPA-based barcoded tracers at ultra-trace level. An automated solid phase extraction (SPE) pre-concentration stage is added to the input of the HPLC, further lowering the detection limit down to a level of 100 pM (pico molar) of tracer molecules. As developed, the complete automated system offers unprecedented sensitivity of detection and accelerated oil field tracer analysis that approaches “near real-time” speed.

Towards the non-invasive assessment of staling in bovine hides with hyperspectral imaging

Microbial spoilage or staling of bovine hides during storage leads to poor leather quality and increased chemical consumption during processing. Conventional microbiological examinations of hide samples which require time-consuming microbe culture cannot be employed as a practical staling detection approach for leather production. Hyperspectral imaging (HSI), featuring fast data acquisition and implementation flexibility has been considered ideal for in-line detection of microbial contamination in Agri- food products. In this study, a linescan hyperspectral imaging system working in a spectral range of 550 nm to 1700 nm was utilized as a rapid and non-destructive technique for predicting the aerobic plate counts (APC) on raw hide samples during storage. Fresh bovine hide samples were stored at 4 °C and 20 °C for 3 days. Every day, hyperspectral images were acquired on both sides for each sample. The APCs were determined simultaneously by conventional microbiological plating method. Leather quality was evaluated by microscopic inspection of grain surfaces, which indicate the acceptable threshold of microbe load on hide samples for leather processing. Partial least squares regression (PLSR) was applied to fit the spectral information extracted from the samples to the logarithmic values of APC to develop microbe load prediction models. All models showed good prediction accuracy, yielding a Rcv2 in the range of 0.74–0.92 and standard error of cross validation (SECV) in the range of 0.61–0.76 %. The prediction capability of the HSI was explored using the model developed with SNV + smoothened pre-processing to spatially predict plate count in the samples. Models established in this study successfully predicted the staling states characterised by bacterial loads on hide samples with low prediction errors. Models, visually, showed the differences in microbial load across the storage time and temperatures. Results illustrate that HSI can be potentially implemented as a non-invasive tool to predict microbe loads in bovine hides before leather processing, so that real-time grading of hides based on staling states can be achieved. This will reduce the cost of leather production and waste management and pave the way for allocating material supply for different production purposes.

The Impact of Water Temperature on In-Line Turbidity Detection

Turbidity measurements are influenced by environmental factors such as water temperature. We designed experiments to study whether water temperature affects in-line turbidity detection and the potential influence mechanism. A turbidity meter installed in-line could self-record data, including the water temperature and turbidity values. From our experimental analysis, we verified the influence of water temperature on the in-line turbidity. Moreover, the temperature coefficient should not be obtained from the experiment directly because the intrinsic impact of in-line turbidity detection does not come from water temperature. Instead, the effect is derived from the optical components’ heat change. When the water temperature change is insignificant, the in-line turbidity deviation caused by the water temperature can be ignored. However, when the water temperature changes substantially, the in-line turbidity sensor should compensate for the temperature drift.

Hydrogen Evolution Electrocatalysis with a Molecular Cobalt Bis(alkylimidazole)methane Complex in DMF: a Critical Activity Analysis.

[Co(HBMIMPh2 )2 ](BF4 )2 (1) [HBMIMPh2 =bis(1-methyl-4,5-diphenyl-1H-imidazol-2-yl)methane] was investigated for its electrocatalytic hydrogen evolution performance in DMF using voltammetry and during controlled potential/current electrolysis (CPE/CCE) in a novel in-line product detection setup. Performances were benchmarked against three reported molecular cobalt hydrogen evolution reaction (HER) electrocatalysts, [Co(dmgBF2 )2 (solv)2 ] (2) (dmgBF2 =difluoroboryldimethylglyoximato), [Co(TPP)] (3) (TPP=5,10,15,20-tetraphenylporphyrinato), and [Co(bapbpy)Cl](Cl) (4) [bapbpy=6,6'-bis-(2-aminopyridyl)-2,2'-bipyridine], showing distinct performances differences with 1 being the runner up in H2 evolution during CPE and the best catalyst in terms of overpotential and Faradaic efficiency during CCE. After bulk electrolysis, for all of the complexes, a deposit on the glassy carbon electrode was observed, and post-electrolysis X-ray photoelectron spectroscopy (XPS) analysis of the deposit formed from 1 demonstrated only a minor cobalt contribution (0.23 %), mainly consisting of Co2+ . Rinse tests on the deposits derived from 1 and 2 showed that the initially observed distinct activity was (partly) preserved for the deposits. These observations indicate that the molecular design of the complexes dictates the features of the formed deposit and therewith the observed activity.

In-line spectroscopy combined with multivariate analysis methods for endpoint determination in column chromatographic adsorption processes for herbal medicine

Objective: In a chromatographic cycle, the adsorption process is a critical unit operation that has a significant impact on downstream processes and, ultimately, the quality of the final products. The development of a rapid method to determine the endpoints of adsorption processes in a large-scale manufacturing is of substantial importance for herbal medicine (HM) manufacturers. Methods: In this study, the adsorption of saponins on a macroporous resin column chromatograph, a critical unit operation in Panax notoginseng (Burkill) F.H.Chen injection manufacturing, was considered as an example. The evaluation results of in-line ultraviolet and visible spectra combined with various multivariate analysis methods, including the moving block standard deviation (MBSD), difference between the moving block average and the target spectrum (DMBA-TS), soft-independent modeling of class analogy (SIMCA), and partial least-squares discriminant analysis (PLS-DA), were compared. Results: MBSD was unsuitable for adsorption processes. The relative standard errors of prediction between the predicted and experimental endpoints were 13.2%, 4.67%, and 5.71% using DMBA-TS, SIMCA, and PLS-DA, respectively. Conclusions: Among the considered analysis methods, SIMCA and PLS-DA were more effective for endpoint determination. The results of this study provide a more comprehensive overview of the effectiveness of various multivariate analysis methods to facilitate the selection of the most suitable method. This study was also conducive to address the issues of the in-line detection of adsorption endpoints to guide practical HM manufacturing. Graphical abstract: http://links.lww.com/AHM/A23

Automatic Defect Detection in Epitaxial Layers by Micro Photoluminescence Imaging

The early in-line detection of defects is a fundamental step in semiconductor manufacturing to ensure the device quality. Inspection techniques currently available can effectively detect large epitaxial defects causing morphological surface variations like stacking faults, while dislocations go undetected. Herein we introduce a new technology with enhanced machine learning analysis, based on contactless and non-destructive room temperature micro-photoluminescence imaging (micro-PL), for the detection and classification of defects in silicon epitaxial layers. With laboratory microscopy techniques we investigate the correspondence between different defect morphologies in micro-PL images and extended crystallographic defects. A good matching in terms of defect density is found between automatic micro-PL analysis and the standard laboratory analysis in an interval spanning from few defects/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> up to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> defects/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Rapid and selective electrochemical sensing of bacterial pneumonia in human sputum based on conductive polymer dot electrodes

A rapid wireless electrochemical biosensor that can discriminate between gram-negative and gram-positive bacteria is designed for the selective detection of pneumonia pathogens in human sputum. The selective binding with the bacterial cell wall of gram-negative and gram-positive bacteria is achieved by utilizing colistin- and vancomycin-conjugated polymer dot-coated electrodes (PD-Colis for gram-negative and PD-Vanco for gram-positive, respectively) and can be observed as changes in resistance (ΔR~12–15 kΩ for PD-Colis and ΔR~13–17 kΩ for PD-Vanco). The PD-Colis- and PD-Vanco-coated electrodes demonstrate high sensitivities determined by the low limit of detection (LOD) for both gram-negative (3.0 CFU/mL, R2=0.995) and gram-positive (3.1 CFU/mL, R2=0.994) bacteria. The electrodes can also be used to detect antibiotic-resistant bacteria, such as carbapenem-resistant K. pneumoniae (CRKP) and methicillin-resistant S. aureus (MRSA), as well as enabling selective detection in complex media such as human serum. Moreover, the sensors based on PD-Colis- and PD-Vanco-coated electrodes show excellent performance in real clinical samples such as human sputum. Finally, the integration of the sensor with a wireless sensing system provides in-line bacterial detection and allows monitoring via a smartphone. We anticipate that the bacterial sensor can be potentially used for the rapid and accurate diagnosis of bacterial infections in the clinic.

Design and Analysis of a Hybrid-Type RF MEMS Phase Detector in X-Band.

In this paper, we have designed, analyzed, and characterized a hybrid-type MEMS device for X-band phase shift measurement. The signal related to a phase shift of the inputs is fractionally in-line coupled by a MEMS beam and delivered to a thermoelectric power sensor, where the phase is ultimately converted into DC voltage output. With the hybrid of the MEMS beam and the thermoelectric power sensor, both in-line detection process and phase-DC voltage conversion is reserved, which is a benefit for large power capacity, good linearity property, and high-level integration density. In order to get a deep insight into the physical mechanisms involved in the phase detection process, a comprehensive analysis model is presented. The beam is modeled as a precise RLC circuit component, where the capacitance is related to the input power. The fabrication is compatible with GaAs monolithic microwave integrated circuit (MMIC) technology. Experimental results show that return loss is smaller than −11.3 dB and isolation is better than −9.3 dB over X-band. Phase shift detection from 0 to 180 degrees is verified for a large power range of 200–1600 mW (23–32 dBm). The perfect linearity property of the phase-detection sensitivity is demonstrated in the same power range. Low intermodulation distortion is also confirmed through measurement. It is revealed from the comparison between this work and other published results in the literature that this presented hybrid-type structure shows superiorities in both power handling ability and phase-detection linearity. It can be adopted in medium power signal applications with a high level of integration.

Towards a real-time tool state detection in sheet metal forming processes validated by wear classification during blanking

The potential of data for inline detection of changes in the physical state of sheet metal forming processes has been proven over the last decade. However, with production rates exceeding 300 parts per minute the time available for a workpiece-related processing of sensor data is reduced. Therefore, the analysis of large data sets is outsourced to the cloud taking advantage of the high computing power provided there. But within this cloud-based computing paradigm, the speed of data transmission hinders real-time analysis of data and causes latency between fault detection and its occurrence. To overcome this bottleneck, this study aims to evaluate a data-based monitoring (DBM) approach for estimating process states in high speed sheet metal forming in terms of their suitability for a decentralized analysis at the edge. Thereby, the DBM is evaluated according to the model accuracy and the absolute computing time. In order to quantify these key performance parameters and the applicability of the DBM on edge devices, a classification of 16 wear states during blanking is considered. Based on the key performance parameters, an optimal DBM approach for decentralized analysis is proposed and an empirical formulation is provided to estimate the absolute computing time depending on the computational resources used for data processing.

Online Monitoring of Small Volume Reactions Using Compact Liquid Chromatography Instrumentation.

A wide variety of analytical techniques have been employed for monitoring chemical reactions, with online instrumentation providing additional benefits compared to offline analysis. A challenge in the past for online monitoring has been placement of the monitoring instrumentation as close as possible to the reaction vessel to maximize sampling temporal resolution and preserve sample composition integrity. Furthermore, the ability to sample very small volumes from bench-scale reactions allows the use of small reaction vessels and conservation of expensive reagents. In this study, a compact capillary LC instrument was used for online monitoring of as small as 1 mL total volume of a chemical reaction mixture, with automated sampling of nL-scale volumes directly from the reaction vessel used for analysis. Analyses to demonstrate short term (~2 h) and long term (~ 50 h) reactions were conducted using tandem on-capillary ultraviolet absorbance followed by in-line MS detection or ultraviolet absorbance detection alone, respectively. For both short term and long term reactions (10 and 250 injections, respectively), sampling approaches using syringe pumps minimized the overall sample loss to ~0.2% of the total reaction volume.

Integrated biosensors for monitoring microphysiological systems.

Microphysiological systems (MPSs), also known as organ-on-a-chip models, aim to recapitulate the functional components of human tissues or organs in vitro. Over the last decade, with the advances in biomaterials, 3D bioprinting, and microfluidics, numerous MPSs have emerged with applications to study diseased and healthy tissue models. Various organs have been modeled using MPS technology, such as the heart, liver, lung, and blood-brain barrier. An important aspect of in vitro modeling is the accurate phenotypical and functional characterization of the modeled organ. However, most conventional characterization methods are invasive and destructive and do not allow continuous monitoring of the cells in culture. On the other hand, microfluidic biosensors enable in-line, real-time sensing of target molecules with an excellent limit of detection and in a non-invasive manner, thereby effectively overcoming the limitation of the traditional techniques. Consequently, microfluidic biosensors have been increasingly integrated into MPSs and used for in-line target detection. This review discusses the state-of-the-art microfluidic biosensors by providing specific examples, detailing their main advantages in monitoring MPSs, and highlighting current developments in this field. Finally, we describe the remaining challenges and potential future developments to advance the current state-of-the-art in integrated microfluidic biosensors.

A 3D-printed, multi-modal microfluidic device for measuring nitric oxide and ATP release from flowing red blood cells.

In this paper, a 3D-printed multi-modal device was designed and fabricated to simultaneously detect nitric oxide (NO) and adenosine triphosphate (ATP) in red blood cell suspensions prepared from whole blood. Once a sample was injected into the device, NO was first detected (via amperometry) using a three-electrode, dual-opposed, electrode configuration with a platinum-black/Nafion coated gold working electrode. After in-line amperometric detection of NO, ATP was detected via a chemiluminescence reaction, with a luciferin/luciferase solution continuously pumped into an integrated mixing T and the resulting light being measured with a PMT underneath the channel. The device was optimized for mixing/reaction conditions, limits of detection (40 nM for NO and 30 nM for ATP), and sensitivity. This device was used to determine the basal (normoxic) levels of NO and ATP in red blood cells, as well as an increase in concentration of both analytes under hypoxic conditions. Finally, the effect of storing red blood cells in a commonly used storage solution was also investigated by monitoring the production of NO and ATP over a three-week storage time.

Process integrated biosensors for real-time monitoring of antibodies for automated affinity purification.

Therapeutic monoclonal antibodies (mAbs) provide new means for treatments of a wide range of diseases and comprise a large fraction of all new approved drugs. Production of mAbs is expensive compared to conventional drug production, primarily due to the complex processes involved. The affinity purification step is dominating the cost of goods in mAb manufacturing. Process intensification and automation could reduce costs, but the lack of real-time process analytical technologies (PAT) complicates this development. We show a specific and robust fiber optical localized surface plasmon resonance (LSPR) sensor technology that is optimized for in-line product detection in the effluent in affinity capture steps. The sensor system comprises a flow cell and a replaceable sensor chip functionalized with biorecognition elements for specific analyte detection. The high selectivity of the sensor enable detection of mAbs in complex sample matrices at concentrations below 2.5 μg mL-1. In place regeneration of the sensor chips allowed for continuous monitoring of multiple consecutive chromatographic separation cycles. Excellent performance was obtained at different purification scales with flow rates up to 200 mL min-1. This sensor technology facilitates efficient column loading, optimization, and control of chromatography systems, which can pave the way for continuous operation and automation of protein purification steps.

Fully Automated Microfluidic Mixing Aided In-Line Detection of Trace Level Oil Field Tracers

Fully Automated Microfluidic Mixing Aided In-Line Detection of Trace Level Oil Field Tracers