Analytical Sciences最新文献

In recent years, wearable sweat sensors have garnered significant attention for real-time monitoring of human physiological information because of their ability to continuously and non-invasively detect multiple sweat biomarkers. Among these, potentiometric sensors stand out for their low power consumption, low cost, compact design, and real-time monitoring capabilities, making them an ideal alternative for sweat analysis. However, enhancing the sensitivity of ion-selective electrodes (ISEs), a critical parameter of potentiometric sensors, remains a challenging research focus. In this work, the sensitivity of K⁺ ISEs was significantly enhanced by doping two-dimensional nanoparticles graphitic carbon nitride (g-C₃N₄) into the ion-to-electron transducer of the electrode via electrodeposition. The calibration curve slope of the K⁺ potentiometric sensors with doped g-C₃N₄ reached 59.6 mV/dec, representing a 33% increase in sensitivity compared to the control sensor without g-C₃N₄. Furthermore, the developed sensors demonstrated excellent repeatability, and anti-interference capabilities. Finally, the feasibility of the prepared sensors was further validated in artificial sweat. The large specific surface area of g-C₃N₄ combined with the excellent conductivity of PEDOT: PSS, significantly improved the sensitivity of ISEs in this study. This innovative approach paves a new avenue for the application of two-dimensional materials in potentiometric sensors, potentially advancing the field of real-time sweat analysis.

The human BK channel (hBK) is an essential membrane protein that regulates various biological functions, and its dysfunction leads to serious diseases. Understanding the biophysical properties of hBK channels is crucial for drug development. Artificial lipid bilayer recording is used to measure biophysical properties at the single-channel level. However, this technique is time-consuming and complicated; thus, its measurement efficiency is very low. Previously, we developed a novel technique to improve the measurement efficiency by rapidly forming lipid bilayer membranes and incorporating ion channels into the membrane using a hydrophilically modified gold probe. To further improve our technique for application to the hBK channel, we combined it using the gold probe with a liposome fusion method. Using a probe on which liposomes containing hBK channels were immobilized, the channels were efficiently incorporated into the lipid bilayer membrane, and the measured channel currents showed the current characteristics of the hBK channel. This technique will be useful for the efficient measurements of the channel properties of hBK and other biologically important channels.

Long noncoding RNAs (lncRNAs) are transcripts exceeding 200 nucleotides that do not encode proteins. Despite lacking protein-coding capabilities, lncRNAs play crucial roles in cellular processes, including gene-expression modulation and structural maintenance. The study of lncRNAs has evolved significantly since 2009, with advancements in analytical methodologies providing new insights into their functions and dynamics. Key developments include BRIC-Seq, SLAM-Seq, TUC-Seq, TimeLapse-seq, and Dyrec-Seq. These methodologies have enabled researchers to investigate lncRNA behavior under various conditions, including cellular stress responses and complex biologic systems. Future challenges include developing comprehensive techniques for identifying lncRNA-interacting proteins and advancing in vivo methodologies using model organisms. As the field progresses, integrating these technologies will enhance our understanding of lncRNA biology, potentially leading to novel therapeutic strategies and deeper insights into gene-regulation mechanisms.

The femtosecond pump-probe technique, i.e. the transient transmission spectroscopy, has been used for the first time, to detect the vibrational spectra of symmetric fundamentals ν₂ and ν₃ in bromoform and chloroform. The spectra were obtained by fast Fourier transforms of the time domain signals. For both, CHCl₃ and CHBr₃, there are four isotopologues contributing to the spectra, due to the existence of two stable isotopes; chlorine, ³⁵Cl and ³⁷Cl, and bromine, ⁷⁹Br and ⁸¹Br, respectively. While for chloroform the isotope splitting of the ν₃ spectral band can be observed even in the spontaneous Raman scattering, for bromoform it is not detectable. Herewith we show that using the time domain spectroscopy and the windowed Fourier transform method we can provide the high resolution spectrum of the ν₃ fundamental in bromoform, in which the contributions of all isotopologues are well distinguishable. The data have been collected for few volume concentrations of the studied liquids diluted in the neutral solvent CCl₄. It is shown that the intensity pattern of the spectra evolves with decreasing concentration and for the ν₃ fundamental it reaches the natural abundance pattern at a very high dilution. The simple theoretical model, which treats the molecules in a liquid as interacting oscillators, allows us to explain the dependence of the shape of the spectrum on the strength of the intermolecular interactions.

We report 532-nm and 1064-nm excited hyper-Raman (HR) spectra of representative non-proteinogenic amino acids, including α-, β-, and γ-amino acids. Different from the common 20 proteinogenic amino acids, natural non-proteinogenic amino acids cannot be incorporated into proteins during translation, while they are indispensable as intermediates in many processes like biosynthesis and neurotransmitters. In 532-nm excited HR spectra, the COO^─ symmetric stretching bands are commonly intense, and the NH₃⁺ bands are clearly observable. In addition, based on the reported IR and Raman study, we found that some HR bands are IR-active but Raman-inactive. In contrast, HR signals with the 1064-nm excitation are much weaker than the 532-nm excitation. Nevertheless, we observed the COO^─ scissoring band unexpectedly, much stronger than other bands with the 1064-nm excitation. Our results suggest that the electronic resonance effect plays a role in enabling us to detect HR signals in the UV region readily. We expect that this study provides a supplementary reference for HR spectroscopy of natural amino acids.

The hydration state of the alcohols was investigated using the extended molar absorption coefficient, which redefines the molar absorption coefficient as a differential coefficient of concentration. The extended molar absorption coefficient is a function of the concentration calculated from the difference in absorbance, and is consistent with the conventional molar absorption coefficient, allowing a complete quantitative comparison. The quantitative performance was verified using IR and NIR absorption spectra of aqueous solutions of monovalent alcohols (methanol, ethanol, 1-propanol, 2-propanol, and tert-butanol) that were soluble in water at any mixing ratio. Extended molar absorption coefficient spectra were calculated for the combination bands of water, which were further separated by multivariate curve resolution-alternating least squares (MCR-ALS) into molecular species with different peak wavenumbers: strongly hydrogen-bonded (SHB), weakly hydrogen-bonded (WHB), and free OH species. The number of water species that change when one alcohol molecule increases, i.e., the perturbed hydration number (PHN), was calculated by comparison with the conventional molar absorption coefficient of pure water. The calculated PHN indicates that the numbers of SHB and WHB species are reversed at approximately 20 wt%, and that the free OH species increase at higher alcohol concentrations and are more pronounced for alcohols with bulky alkyl groups. These results provide a quantitative answer to the long-debated question of anomalies in water-alcohol mixing.

The analysis of Raman spectrum data has gradually transitioned into the era of machine learning. However, it is still constrained by the challenge of acquiring large volumes of raw data and the issue of losing characteristic information from spectral data. In this paper, we propose a strategy that combines data amplification and attention mechanisms for analyzing antibiotic spectral data. Firstly, a Generative Adversarial Network was employed to amplify the SERS spectrum of eight antibiotics by 10 times, to augment the dataset to fulfill the requirements of the neural network. Then, the amplified data is input into a one-dimensional convolutional neural network with an attentional mechanism module, which enables a more accurate capture of spectral feature information. The one-dimensional convolutional neural network achieved a 97.5% accuracy in classifying eight antibiotics. The accuracy of the four mixtures within the same class was 89.4%.

Oligosaccharides were covalently labeled with 1-pyrenemethylamine (1-PMA) in 15 min at 75 °C with high yield, separated by quantitative HPLC in less than 20 min, and characterized by off-line Matrix-Assisted Laser Desorption/Ionization (MALDI) Time-of-Flight (TOF) mass spectrometry (MS). A new MALDI mass spectral precursor ion for fragmentation studies was observed from 2,5-dihydroxybenzoic acid matrix (DHB, hydroquinone/benzoquinone redox system) through photo-induced reductive elimination. The resulting high-energy protonated glycosylamine provided excellent fragmentation efficiency (Y-, B-ions, and combination losses), with ions covering almost the entire structure of several oligosaccharides at the low picomol level. With the new method, glycans from commercially available standard glycoproteins and glycans from bioengineered β-lactoglobulin expressed in the bgs13 mutant of Pichia pastoris were analyzed.

Escherichia coli (E. coli) is a pathogen that has generated global concern due to the public health challenges it has created. Therefore, the rapid and accurate detection of E. coli is important to public health safety. Microchips have become a popular analytical technique for detecting E. coli due to their automation, high analytical efficiency, and low analyte consumption. Therefore, this paper highlights multiple microchip-based strategies for the detection of E. coli, reviews their limitations, and provides strategies and future perspectives for analyzing E. coli..

In this study, a simple, accurate, and precise analytical strategy was developed for the determination of total nitrogen (total dissolved nitrogen + particulate nitrogen) in surface waters using smartphone technology. The spectrophotometric method based on digestion with persulfate, commonly used in water analyses, was integrated with a digital image-based colorimetric detection system (DIC). To evaluate the accuracy of the total nitrogen-digital image-based colorimetric detection system (TN-DIC), results obtained from the Hach method and UV-VIS spectrophotometer were compared with the RGB values obtained from the smartphone imaging system. T-Test results indicated no statistically significant difference between the results obtained from the TN-DIC method and the standard methods. The linear range of the TN-DIC method was determined to be 0.25-5 mg/L. The analytical quality indicators of the system were established as follows: limit of detection (LOD) 0.095 ppm, limit of quantification (LOQ) 0.29 ppm, %RSD 0.32, and R² value 0.9982. Recovery percentages (99-112%) used to measure the analytical performance of the method were calculated through spike applications at concentrations of 1 and 2 ppm in real water samples. Unlike traditional spectrophotometric methods, this smartphone-based approach is fast, simple, and cost-effective, requiring no complex equipment during analysis.