Pyrophosphate Sensor Based on Principal Component Analysis of Conjugated Polyelectrolyte Fluorescence

Multivariable System Prediction Based on TCN-LSTM Networks with Self-Attention Mechanism and LASSO Variable Selection

Intelligent prediction of key output variables that are difficult to measure online in complex systems has important research significance. In this paper, by using the least absolute shrinkage and selection operator (LASSO) algorithm to analyze the principal elements of input variables, a temporal convolutional network fused with long short-term memory (TCN-LSTM) network and self-attention mechanism (SAM) is designed to realize dynamic modeling of multivariate feature sequences. For complex processes with multiple input variables, each variable has different effects on the output, so it is necessary to use the LASSO algorithm to perform regression analysis on the input and output data for selecting the principal component variables and reducing the redundancy and computation burden of the network. The TCN network is used to extract the features of the input variables efficiently. The long-term memory performance of time series is enhanced by applying an LSTM network. The multihead SAM is used to optimize the network, and the role of key features is enhanced by assigning weights with probability to further improve the accuracy of sequence prediction. Finally, by comparison with the existing network model, the offline data generated by the high and low converters in the synthetic ammonia industry is used to predict the CO content so as to verify the superiority and applicability of the proposed network model.

Networks of as-dispersed, polymer-wrapped (6,5) single-walled carbon nanotubes for selective Cu2+ and glyphosate sensing

Networks of semiconducting single-walled carbon nanotubes (SWNTs) can be used as the transducing layer for sensors based on water-gated transistors. To add specific sensing capabilities, SWNTs are often functionalized with additional moieties or selective membranes are applied, thus increasing the complexity of the fabrication process. Here we demonstrate that drop-cast networks of monochiral (6,5) SWNTs, which are commonly dispersed in organic solvents with the polyfluorene-bipyridine copolymer PFO-BPy, can be employed directly and without additional functionalization or ion-selective membranes to detect Cu²⁺ ions over a wide range of concentrations in aqueous solutions. The observed voltage shifts of water-gated transistors with these (6,5) SWNT networks directly correlate with the cupric ion concentration. They result from induced n-doping due to the complexation of positive copper ions to the bipyridine units of the wrapping polymer. Furthermore, the competitive binding of Cu²⁺ to the herbicide glyphosate as well as to biologically relevant pyrophosphates can be used for the direct detection and quantification of these molecules at nano- to micromolar concentrations.

A micellized fluorescence sensor based on amplified quenching for highly sensitive detection of non-transferrin-bound iron in serum

A novel, micelle-based fluorescence system capable of selective and sensitive signal transduction for non-transferrin-bound iron (NTBI) in serum was devised. We demonstrated that an aggregated fluorogenic core in a micelle induces amplified fluorescence quenching, which differentiates this sensing platform from the numerous fluorescence-based tools previously developed for sensitive detection.

Spinel Cobalt Ferrite Nanoparticles for Sensing Phosphate Ions in Aqueous Media and Biological Samples

Phosphate ions perform a variety of functions in metabolic processes and are essential for all living organisms. The determination of the concentration of phosphate ions is useful in clinical diagnosis of various diseases as an inadequate phosphate level could lead to many health problems. In the search for a cost-effective method of fast monitoring, we investigated the use of cobalt ferrite nanoparticles (CoFeNPs) in the selective recognition of phosphate ions dissolved in aqueous media and more complex samples, such as human blood serum. We prepared these NPs by a chemical coprecipitation route and subjected them to annealing at 600 °C for 1 h. The successful formation of the NPs was confirmed by Fourier transform infrared spectroscopy, X-ray diffraction, transmission electron microscopy, and hysteresis loop measurements. The NPs exhibited a ferrimagnetic behavior, a spinel-type crystalline structure, and hexagonal shape in the nanoscale range. We demonstrated that CoFeNPs containing immobilized fluorescent-labeled single-chain DNA (ssDNA*) probes can be applied for the fast selective detection of phosphate ions dissolved in a liquid medium. We have explored the fact that phosphate groups can displace ssDNA* probes attached to the nanoparticles, therefore causing a perceptible change in the fluorescence signal of the supernatant liquid. This detection method has been tested for the sensing of phosphate ions present both in aqueous solutions and in biological samples, with excellent selectivity and a low limit of detection (∼1.75 nM).

Prospects and Challenges of Volatile Organic Compound Sensors in Human Healthcare

The chemical signatures of volatile organic compounds (VOCs) in humans can be utilized for point-of-care (POC) diagnosis. Apart from toxic exposure studies, VOCs generated in humans can provide insights into one's healthy and diseased metabolic states, acting as a biomarker for identifying numerous diseases noninvasively. VOC sensors and the technology of e-nose have received significant attention for continuous and selective monitoring of various physiological and pathophysiological conditions of an individual. Noninvasive detection of VOCs is achieved from biomatrices of breath, sweat and saliva. Among these, detection from sweat and saliva can be continuous in real-time. The sensing approaches include optical, chemiresistive and electrochemical techniques. This article provides an overview of such techniques. These, however, have limitations of reliability, precision, selectivity, and stability in continuous monitoring. Such limitations are due to lack of sensor stability and complexity of samples in a multivariate environment, which can lead to false readings. To overcome selectivity barriers, sensor arrays enabling multimodal sensing, have been used with pattern recognition techniques. Stability and precision issues have been addressed through advancements in nanotechnology. The use of various forms of nanomaterial not only enhance sensing performance, but also plays a major role in detection on a miniaturized scale. The rapid growth in medical Internet of Things (IoT) and artificial intelligence paves a pathway for improvements in human theranostics.

High-affinity and selective detection of pyrophosphate in water by a resorcinarene salt receptor

Pyrophosphate (PPi) is a byproduct of DNA and RNA synthesis, and abnormal levels are indicative of disease. We report the high-affinity binding of PPi in water by N-alkyl ammonium resorcinarene chloride receptors. Experimental analysis using ¹H and ³¹P NMR, isothermal titration calorimetry, mass spectrometry, and UV-vis spectroscopy all support exceptional selectivity of these systems for PPi in water. The measured affinity of K₁ = 1.60 × 10⁷ M^-1 for PPi is three orders of magnitude larger than that observed for binding to another phosphate, ATP. This exceptional anion-binding affinity in water is explored through a detailed density functional theory computational study. These systems provide a promising avenue for the development of future innovative medical diagnostic tools.