Hydrophobic MOF/PDMS-Based QCM Sensors for VOCs Identification and Quantitative Detection in High-Humidity Environments.

Metal-organic frameworks (MOFs) have great potential in quartz crystal microbalance (QCM) platforms for volatile organic compound (VOCs) detection and recognition due to their unique properties. However, the MOFs' hydrophilicity degrades performance in high-humidity environments, limiting reliable VOC sensing in complex environments. Herein, we propose a novel VOC virtual sensor array (VSA) using a single QCM sensor with an adsorption layer composed of MIL-101(Cr) MOF and polydimethylsiloxane (PDMS), realizing stable sensing and accurate identification for different VOCs under various relative humidity (RH) conditions. The hydrophobic PDMS layer improves the moisture resistance of the sensor to 4 and 14 times in terms of shifts in resonant frequency and scattering parameters, respectively. In addition, performance is maintained over 2 days of water treatment, demonstrating superior water resistance. The highest sensitivity of 2.68 mdB ppm-1 is achieved for isopropanol detection, with the lowest limit of detection of 20.06 ppm for acetone. Combining resonant signals and lumped parameters, the proposed VSA technique effectively discriminates four VOCs (ethanol, 2-propanol, acetone, and acetonitrile) with a high accuracy of 95.3% under both 60% and 90% RH backgrounds. The studies provide a promising solution for reliable low-concentration VOC detection using QCM sensors in high-humidity environments such as underground spaces.

Study on the detection method of biological characteristics of hepatoma cells based on terahertz time-domain spectroscopy.

Liver cancer usually has a high degree of malignancy and its early symptoms are hidden, therefore, it is of significant research value to develop early-stage detection methods of liver cancer for pathological screening. In this paper, a biometric detection method for living human hepatocytes based on terahertz time-domain spectroscopy was proposed. The difference in terahertz response between normal and cancer cells was analyzed, including five characteristic parameters in the response, namely refractive index, absorption coefficient, dielectric constant, dielectric loss and dielectric loss tangent. Based on class separability and variable correlation, absorption coefficient and dielectric loss were selected to better characterize cellular properties. Maximum information coefficient and principal component analysis were employed for feature extraction, and a cell classification model of support vector machine was constructed. The results showed that the algorithm based on parameter feature fusion can achieve an accuracy of 91.6% for human hepatoma cell lines and one normal cell line. This work provides a promising solution for the qualitative evaluation of living cells in liquid environment.

Microfluidic Channel-Based Soft Electrodes and Their Application in Capacitive Pressure Sensing.

Flexible and stretchable electrodes are essential components in soft artificial sensory systems. Despite recent advances in flexible electronics, most electrodes are either restricted by the patterning resolution or the capability of inkjet printing with high-viscosity super-elastic materials. In this paper, we present a simple strategy to fabricate microchannel-based stretchable composite electrodes, which can be achieved by scraping elastic conductive polymer composites (ECPCs) into lithographically embossed microfluidic channels. The ECPCs were prepared by a volatile solvent evaporation method, which achieves a uniform dispersion of carbon nanotubes (CNTs) in a polydimethylsiloxane (PDMS) matrix. Compared to conventional fabrication methods, the proposed technique can facilitate the rapid fabrication of well-defined stretchable electrodes with high-viscosity slurry. Since the electrodes in this work were made up of all-elastomeric materials, strong interlinks can be formed between the ECPCs-based electrodes and the PDMS-based substrate at the interfaces of the microchannel walls, which allows the electrodes to exhibit mechanical robustness under high tensile strains. In addition, the mechanical-electric response of the electrodes was also systematically studied. Finally, a soft pressure sensor was developed by combining a dielectric silicone foam and an interdigitated electrodes (IDE) layer, and this demonstrated great potential for pressure sensors in soft robotic tactile sensing applications.

Sensitivity Enhancement of Soft Capacitive Pressure Sensors Using a Solvent Evaporation-Based Porosity Control Technique.

Soft pressure sensors play a significant role in developing "man-machine" tactile sensation in soft robotics and haptic interfaces. Specifically, capacitive sensors with micro-structured polymer matrices have been explored with considerable effort because of their high sensitivity, wide linearity range, and fast response time. However, the improvement of the sensing performance often relies on the structural design of the dielectric layer, which requires sophisticated microfabrication facilities. This article reports a simple and low-cost method to fabricate porous capacitive pressure sensors with improved sensitivity using the solvent evaporation-based method to tune the porosity. The sensor consists of a porous polydimethylsiloxane (PDMS) dielectric layer bonded with top and bottom electrodes made of elastic conductive polymer composites (ECPCs). The electrodes were prepared by scrape-coating carbon nanotubes (CNTs)-doped PDMS conductive slurry into mold-patterned PDMS films. To optimize the porosity of the dielectric layer for enhanced sensing performance, the PDMS solution was diluted with toluene of different mass fractions instead of filtering or grinding the sugar pore-forming agent (PFA) into different sizes. The evaporation of the toluene solvent allowed the fast fabrication of a porous dielectric layer with controllable porosities. It was confirmed that the sensitivity could be enhanced more two-fold when the toluene to PDMS ratio was increased from 1:8 to 1:1. The research proposed in this work enables a low-cost method of fabricating fully integrated bionic soft robotic grippers with soft sensory mechanoreceptors of tunable sensor parameters.

Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method.

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a method for detecting cyanobacterial blooms based on a deep-learning algorithm. An improved vegetation-index method based on a multispectral image taken by an Unmanned Aerial Vehicle (UAV) was adopted to extract inconspicuous spectral features of cyanobacterial blooms. To enhance the recognition accuracy of cyanobacterial blooms in complex scenes with noise such as reflections and shadows, an improved transformer model based on a feature-enhancement module and pixel-correction fusion was employed. The algorithm proposed in this paper was implemented in several rivers in China, achieving a detection accuracy of cyanobacterial blooms of more than 85%. The estimate of the proportion of the algae bloom contamination area and the severity of pollution were basically accurate. This paper can lay a foundation for ecological and environmental departments for the effective prevention and control of cyanobacterial blooms.

Characterization and discrimination of human colorectal cancer cells using terahertz spectroscopy.

Terahertz technology has been widely used in biomedical research. Herein, terahertz time-domain attenuated total reflection (THz TD-ATR) spectroscopy was employed to characterize and discriminate human cancer cell lines (DLD-1 and HT-29). Terahertz responses of the cell lines were measured and Savitzky-Golay algorithm was applied to smooth the spectra of refractive index, absorption coefficient and dielectric loss tangent in terahertz regime. Principal component analysis (PCA) was then adopted for feature extraction and cell characterization. Based on the processed data, cancer cell lines were discriminated by applying random forests (RF) method to analyze three characteristic parameters separately and the results from them were compared. Results indicate that absorption coefficient was the most sensitive parameter for cancer cell discrimination. Our study suggests great potential for human cancer cell recognition and provides experimental basis for liquid biopsy.

Qualitative and quantitative detection of liver injury with terahertz time-domain spectroscopy.

Terahertz technology has been widely used as a nondestructive and effective detection method. Herein, terahertz time-domain spectroscopy was used to detect drug-induced liver injury in mice. Firstly, the boxplots were used to detect abnormal data. Then the maximal information coefficient method was used to search for the features strongly correlated with the degree of injury. After that, the liver injury model was built using the random forests method in machine learning. The results show that this method can effectively identify the degree of liver injury and thus provide an auxiliary diagnostic method for detecting minor liver injury.

Study on glycoprotein terahertz time-domain spectroscopy based on composite multiscale entropy feature extraction method.

Tumor genesis is accompanied by glycosylation of related proteins. Glycoprotein is usually regarded as a tumor marker since glycoproteins are consumed remarkably more by the cancer cells than the normal ones. In this paper, the terahertz time-domain attenuated total reflection (ATR) technique is applied to inspect the glycoprotein solution from a concentration gradient of 0.2 mg/ml to 50 mg/ml. A significant nonlinear relationship between the absorption coefficient and the concentrations has been discovered. The influence of the dynamical hydration shell around glycoprotein molecules on the absorption coefficient is discussed and the phenomenon is explained by the concepts of THz excess and THz defect. In order to identify glycoproteins, features are obtained by composite multiscale entropy (CMSE) method and clustered by the K-means algorithm. The results indicate that features extracted by the CMSE method are better than the Principal Component Analysis (PCA) method in both specificity and sensitivity of recognition. Meanwhile, the absorption coefficient and dielectric loss angle tangent are more suitable for qualitative identification. Research shows that the CMSE method has important directive significance for analyzing glycoprotein terahertz spectroscopy. And it has the potential for glycoprotein related tumor markers identification using terahertz technology in medical applications.

Analysis and inspection techniques for mouse liver injury based on terahertz spectroscopy.

At present, researchers are exploring biological tissue detection method using terahertz techniques. In this paper, techniques to inspect mouse liver injury by using terahertz spectroscopy were studied. The boxplots were applied to remove abnormal data, and the maximal information coefficient was employed to select crucial features from the absorption coefficient and refractive index spectra. Random Forests and AdaBoost were applied to recognize different levels of liver injury. We found that AdaBoost had better performance on low-level injury classification. This work suggests that terahertz techniques have the potential to detect liver injury at an early stage and evaluate liver treatment strategies.

Inspecting human colon adenocarcinoma cell lines by using terahertz time-domain reflection spectroscopy.

Techniques to inspect and analyze human colorectal cancer cell lines by using terahertz time-domain attenuated total reflection spectroscopy (THz TD-ATR) were investigated. The characteristics of THz absorption spectra of two colorectal cancer cell lines DLD-1 and HT-29 in aqueous solutions with different concentrations were studied. Different spectral features were observed compared to normal cell line. Identification results based on different parameters including absorption coefficient, refractive index, real and imaginary parts of complex permittivity, dielectric loss tangent were discussed. This research may be promising for quick and instant inspection of liquid samples by using THz time-domain spectroscopy in medical applications.

Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters.

In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems.

Terahertz spectral unmixing based method for identifying gastric cancer.

At present, many researchers are exploring biological tissue inspection using terahertz time-domain spectroscopy (THz-TDS) techniques. In this study, based on a modified hard modeling factor analysis method, terahertz spectral unmixing was applied to investigate the relationships between the absorption spectra in THz-TDS and certain biomarkers of gastric cancer in order to systematically identify gastric cancer. A probability distribution and box plot were used to extract the distinctive peaks that indicate carcinogenesis, and the corresponding weight distributions were used to discriminate the tissue types. The results of this work indicate that terahertz techniques have the potential to detect different levels of cancer, including benign tumors and polyps.

In situ detection of water quality contamination events based on signal complexity analysis using online ultraviolet-visible spectral sensor.

The contaminant detection in water distribution systems is essential to protect public health from potentially harmful compounds resulting from accidental spills or intentional releases. As a noninvasive optical technique, ultraviolet-visible (UV-Vis) spectroscopy is investigated for detecting contamination events. However, current methods for event detection exhibit the shortcomings of noise susceptibility. In this paper, a new method that has less sensitivity to noise was proposed to detect water quality contamination events by analyzing the complexity of the UV-Vis spectrum series. The proposed method applied approximate entropy (ApEn) to measure spectrum signals' complexity, which made a distinction between normal and abnormal signals. The impact of noise was attenuated with the help of ApEn's insensitivity to signal disturbance. This method was tested on a real water distribution system data set with various concentration simulation events. Results from the experiment and analysis show that the proposed method has a good performance on noise tolerance and provides a better detection result compared with the autoregressive model and sequential probability ratio test.

Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data.

The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data. Firstly, the spectrum of each observation is transformed using discrete wavelet with a coiflet mother wavelet to capture the abrupt change along the wavelength. Principal component analysis is then employed to approximate the spectra based on capture and fusion features. The significant value of Hotelling's T2 statistics is calculated and used to detect outliers. An alarm of contamination event is triggered by sequential Bayesian analysis when the outliers appear continuously in several observations. The effectiveness of the proposed procedure is tested on-line using a pilot-scale setup and experimental data.

Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors.

Water quality early warning system is mainly used to detect deliberate or accidental water pollution events in water distribution systems. Identifying the types of pollutants is necessary after detecting the presence of pollutants to provide warning information about pollutant characteristics and emergency solutions. Thus, a real-time contaminant classification methodology, which uses the multi-classification support vector machine (SVM), is proposed in this study to obtain the probability for contaminants belonging to a category. The SVM-based model selected samples with indistinct feature, which were mostly low-concentration samples as the support vectors, thereby reducing the influence of the concentration of contaminants in the building process of a pattern library. The new sample points were classified into corresponding regions after constructing the classification boundaries with the support vector. Experimental results show that the multi-classification SVM-based approach is less affected by the concentration of contaminants when establishing a pattern library compared with the cosine distance classification method. Moreover, the proposed approach avoids making a single decision when classification features are unclear in the initial phase of injecting contaminants.

THz spectral data analysis and components unmixing based on non-negative matrix factorization methods.

In many situations the THz spectroscopic data observed from complex samples represent the integrated result of several interrelated variables or feature components acting together. The actual information contained in the original data might be overlapping and there is a necessity to investigate various approaches for model reduction and data unmixing. The development and use of low-rank approximate nonnegative matrix factorization (NMF) and smooth constraint NMF (CNMF) algorithms for feature components extraction and identification in the fields of terahertz time domain spectroscopy (THz-TDS) data analysis are presented. The evolution and convergence properties of NMF and CNMF methods based on sparseness, independence and smoothness constraints for the resulting nonnegative matrix factors are discussed. For general NMF, its cost function is nonconvex and the result is usually susceptible to initialization and noise corruption, and may fall into local minima and lead to unstable decomposition. To reduce these drawbacks, smoothness constraint is introduced to enhance the performance of NMF. The proposed algorithms are evaluated by several THz-TDS data decomposition experiments including a binary system and a ternary system simulating some applications such as medicine tablet inspection. Results show that CNMF is more capable of finding optimal solutions and more robust for random initialization in contrast to NMF. The investigated method is promising for THz data resolution contributing to unknown mixture identification.

Distribution water quality anomaly detection from UV optical sensor monitoring data by integrating principal component analysis with chi-square distribution.

The issue of distribution water quality security ensuring is recently attracting global attention due to the potential threat from harmful contaminants. The real-time monitoring based on ultraviolet optical sensors is a promising technique. This method is of reagent-free, low maintenance cost, rapid analysis and wide cover range. However, the ultraviolet absorption spectra are of large size and easily interfered. While within the on-site application, there is almost no prior knowledge like spectral characteristics of potential contaminants before determined. Meanwhile, the concept of normal water quality is also varying due to the operating condition. In this paper, a procedure based on multivariate statistical analysis is proposed to detect distribution water quality anomaly based on ultraviolet optical sensors. Firstly, the principal component analysis is employed to capture the main variety features from the spectral matrix and reduce the dimensionality. A new statistical variable is then constructed and used for evaluating the local outlying degree according to the chi-square distribution in the principal component subspace. The possibility of anomaly of the latest observation is calculated by the accumulation of the outlying degrees from the adjacent previous observations. To develop a more reliable anomaly detection procedure, several key parameters are discussed. By utilizing the proposed methods, the distribution water quality anomalies and the optical abnormal changes can be detected. The contaminants intrusion experiment is conducted in a pilot-scale distribution system by injecting phenol solution. The effectiveness of the proposed procedure is finally testified using the experimental spectral data.

Terahertz spectroscopic investigation of human gastric normal and tumor tissues.

Human dehydrated normal and cancerous gastric tissues were measured using transmission time-domain terahertz spectroscopy. Based on the obtained terahertz absorption spectra, the contrasts between the two kinds of tissue were investigated and techniques for automatic identification of cancerous tissue were studied. Distinctive differences were demonstrated in both the shape and amplitude of the absorption spectra between normal and tumor tissue. Additionally, some spectral features in the range of 0.2~0.5 THz and 1~1.5 THz were revealed for all cancerous gastric tissues. To systematically achieve the identification of gastric cancer, principal component analysis combined with t-test was used to extract valuable information indicating the best distinction between the two types. Two clustering approaches, K-means and support vector machine (SVM), were then performed to classify the processed terahertz data into normal and cancerous groups. SVM presented a satisfactory result with less false classification cases. The results of this study implicate the potential of the terahertz technique to detect gastric cancer. The applied data analysis methodology provides a suggestion for automatic discrimination of terahertz spectra in other applications.

A real-time, dynamic early-warning model based on uncertainty analysis and risk assessment for sudden water pollution accidents.

A real-time, dynamic, early-warning model (EP-risk model) is proposed to cope with sudden water quality pollution accidents affecting downstream areas with raw-water intakes (denoted as EPs). The EP-risk model outputs the risk level of water pollution at the EP by calculating the likelihood of pollution and evaluating the impact of pollution. A generalized form of the EP-risk model for river pollution accidents based on Monte Carlo simulation, the analytic hierarchy process (AHP) method, and the risk matrix method is proposed. The likelihood of water pollution at the EP is calculated by the Monte Carlo method, which is used for uncertainty analysis of pollutants' transport in rivers. The impact of water pollution at the EP is evaluated by expert knowledge and the results of Monte Carlo simulation based on the analytic hierarchy process. The final risk level of water pollution at the EP is determined by the risk matrix method. A case study of the proposed method is illustrated with a phenol spill accident in China.