Determination of Nitrite Using UV Absorption Spectra Based on Multiple Linear Regression

Isotherm model discrimination for multimodal chromatography using mechanistic models derived from high-throughput batch isotherm data

In this work, we have examined an array of isotherm formalisms and characterized them based on their relative complexities and predictive abilities with multimodal chromatography. The set of isotherm models studied were all based on the stoichiometric displacement framework, with considerations for electrostatic interactions, hydrophobic interactions, and thermodynamic activities. Isotherm parameters for each model were first determined through twenty repeated fits to a set of mAb - Capto MMC batch isotherm data spanning a range of loading, ionic strength, and pH as well as a set of mAb - Capto Adhere batch data at constant pH. The batch isotherm data were used in two ways-spanning the full range of loading or consisting of only the high concentration data points. Predictive ability was defined through the model's capacity to capture prominent changes in salt gradient elution behavior with respect to pH for Capto MMC or unique elution patterns and yield losses with respect to gradient slope for Capto Adhere. In both cases, model performance was quantified using a scoring metric based on agreement in peak characteristics for column predictions and accuracy of fit for the batch data. These scores were evaluated for all twenty isotherm fits and their corresponding column predictions, thereby producing a statistical distribution of model performances. Model complexity (number of isotherm parameters) was then considered through use of the Akaike information criterion (AIC) calculated from the score distributions. While model performance for Capto MMC benefitted substantially from removal of low protein concentration data, this was not the case for Capto Adhere; this difference was likely due to the qualitatively different shapes of the isotherms between the two resins. Surprisingly, the top-performing (high accuracy with minimal number of parameters) isotherm model was the same for both resins. The extended steric mass action (SMA) isotherm (containing both protein-salt and protein-protein activity terms) accurately captured both the pH-dependent elution behavior for Capto MMC as well as loss in protein recovery with increasing gradient slope for Capto Adhere. In addition, this isotherm model achieved the highest median score in both resin systems, despite it lacking any explicit hydrophobic stoichiometric terms. The more complex isotherm models, which explicitly accounted for both electrostatic and hydrophobic interaction stoichiometries, were ill-suited for Capto MMC and had lower AIC model likelihoods for Capto Adhere due to their increased complexity. Interestingly, the ability of the extended SMA isotherm to predict the Capto Adhere results was largely due to the protein-salt activity coefficient, as determined via isotherm parameter sensitivity analyses. Further, parametric studies on this parameter demonstrated that it had a major impact on both binding affinity and elution behavior, therein fully capturing the impact of hydrophobic interactions. In summary, we were able to determine the isotherm formalisms most capable of consistently predicting a wide range of column behavior for both a multimodal cation-exchange and multimodal anion-exchange resin with high accuracy, while containing a minimized set of model parameters.

Effect of Nanoparticles on the Thermal Stability and Reaction Kinetics in Ionic Nanofluids

Nowadays, the incorporation of nanoparticles into thermal fluids has become one of the most suitable strategies for developing high-performance fluids. An unconventional improvement of thermo–physical properties was observed with the addition of 1% wt. of nanoparticles in different types of fluids, such as molten salts, allowing for the design of more thermally efficient systems using nanofluids. Despite this, there is a lack of knowledge about the effect that nanoparticles produce on the thermal stability and the decomposition kinetics of the base fluid. The present study performs IR- and UV-vis spectroscopy along with thermogravimetric analysis (TGA) of pure nitrate and nitrate based nanofluids with the presence of SiO₂ and Al₂O₃ nanoparticles (1% wt.). The results obtained support that nanoparticles accelerate the nitrate to nitrite decomposition at temperatures below 500 °C (up to 4%), thus confirming the catalytic role of nanoparticles in nanofluids.

Development of an Optical Method to Monitor Nitrification in Drinking Water

Nitrification is a common issue observed in chloraminated drinking water distribution systems, resulting in the undesirable loss of monochloramine (NH₂Cl) residual. The decay of monochloramine releases ammonia (NH₃), which is converted to nitrite (NO₂^-) and nitrate (NO₃^-) through a biological oxidation process. During the course of monochloramine decay and the production of nitrite and nitrate, the spectral fingerprint is observed to change within the wavelength region sensitive to these species. In addition, chloraminated drinking water will contain natural organic matter (NOM), which also has a spectral fingerprint. To assess the nitrification status, the combined nitrate and nitrite absorbance fingerprint was isolated from the total spectra. A novel method is proposed here to isolate their spectra and estimate their combined concentration. The spectral fingerprint of pure monochloramine solution at different concentrations indicated that the absorbance difference between two concentrations at a specific wavelength can be related to other wavelengths by a linear function. It is assumed that the absorbance reduction in drinking water spectra due to monochloramine decay will follow a similar pattern as in ultrapure water. Based on this criteria, combined nitrate and nitrite spectra were isolated from the total spectrum. A machine learning model was developed using the support vector regression (SVR) algorithm to relate the spectral features of pure nitrate and nitrite with their concentrations. The model was used to predict the combined nitrate and nitrite concentration for a number of test samples. Out of these samples, the nitrified sample showed an increasing trend of combined nitrate and nitrite productions. The predicted values were matched with the observed concentrations, and the level of precision by the method was ± 0.01 mg-N L^-1. This method can be implemented in chloraminated distribution systems to monitor and manage nitrification.

An efficient and robust procedure to calculate absorption spectra of aqueous charged species applied to NO2. Phys Chem Chem Phys 2021;23:14857-14872. [PMID: 34223573 DOI: 10.1039/d1cp00652e]

Accurate calculation of absorption spectra of aqueous NO2- requires rigorously sampling the quantum potential energy surfaces for microsolvation of NO2- with at least five explicit water molecules and embedding the resulting clusters in a continuum solvent accounting for the statistical weighted contributions of individual isomers. This method, which we address as ASCEC + PCM, introduces several desired features when compared against MD simulations derived QM/MM spectra: comparatively fewer explicit solvent molecules to be treated with expensive QM methods, the identification of equilibrium structures in the quantum PES to be used in further vibrational spectroscopy, and the unequivocal identification of cluster orbitals undergoing electronic transitions and charge transfer that originate the spectral bands.

High-precision methane isotopic abundance analysis using near-infrared absorption spectroscopy at 100 Torr

A near-infrared methane (CH₄) sensor system for carbon isotopic abundance analysis was developed based on laser absorption spectroscopy (LAS). For good thermal stability, two CH₄ absorption lines with a similar low-state energy level were selected to realize relative weak temperature dependence. Wavelet denoising (WD) was employed for a pre-treatment of the direct absorption spectral (DAS) signal to perform a preliminary suppression of high-frequency noise. Due to the abnormal ¹³CH₄ profile caused by superimposition of multiple lines, two statistical analysis algorithms including linear regression and neural network prediction were respectively employed on the retrieval of molecule fractions instead of the traditionally used standard absorption line fitting method. Performance assessment and a comparison between the two methods were carried out. Compared with the concentration deducing method based on the maximum absorbance in rough data, the linear regression and the neural network prediction obtained a sensitivity enhancement by ∼2 times and ∼10 times, respectively. A simultaneous measurement of pressure and concentration was performed using the neural network, which indicated a good potential of the technique for multi-parameter analysis using a single LAS-based sensor system.

Development of a Real-Time, Mobile Nitrate Monitoring Station for High-Frequency Data Collection

A mobile monitoring station was developed to measure nitrate and physicochemical water quality parameters remotely, in real-time, and at very high frequencies (thirty minutes). Several calibration experiments were performed to validate the outputs of a real-time nutrient sensor, which can be affected by optical interferences such as turbidity, pH, temperature and salinity. Whilst most of these proved to play a minor role, a data-driven compensation model was developed to account for turbidity interferences. The reliability of real-time optical sensors has been questioned previously; however, this study has shown that following compensation, the readings can be more accurate than traditional laboratory-based equipment. In addition, significant benefits are offered by monitoring waterways at high frequencies, due to rapid changes in analyte concentrations over short time periods. This, combined with the versatility of the mobile station, provides opportunities for several beneficial monitoring applications, such as of fertiliser runoff in agricultural areas in rural regions, aquaculture runoff, and waterways in environmentally sensitive areas such as the Great Barrier Reef.

Self-Healing Performance of Multifunctional Polymeric Smart Coatings

Multifunctional nanocomposite coatings were synthesized by reinforcing a polymeric matrix with halloysite nanotubes (HNTs) loaded with corrosion inhibitor (NaNO₃) and urea formaldehyde microcapsules (UFMCs) encapsulated with a self-healing agent (linseed oil (LO)). The developed polymeric nanocomposite coatings were applied on the polished mild steel substrate using the doctor’s blade technique. The structural (FTIR, XPS) and thermogravimetric (TGA) analyses reveal the loading of HNTs with NaNO₃ and encapsulation of UFMCs with linseed oil. It was observed that self-release of the inhibitor from HNTs in response to pH change was a time dependent process. Nanocomposite coatings demonstrate decent self-healing effects in response to the external controlled mechanical damage. Electrochemical impedance spectroscopic analysis (EIS) indicates promising anticorrosive performance of novel nanocomposite coatings. Observed corrosion resistance of the developed smart coatings may be attributed to the efficient release of inhibitor and self-healing agent in response to the external stimuli. Polymeric nanocomposite coatings modified with multifunctional species may offer suitable corrosion protection of steel in the oil and gas industry.

A Review of State-of-the-Art Microfluidic Technologies for Environmental Applications: Detection and Remediation

Microfluidic systems have advanced beyond natural and life science applications and lab-on-a-chip uses. A growing trend of employing microfluidic technologies for environmental detection has emerged thanks to the precision, time-effectiveness, and cost-effectiveness of advanced microfluidic systems. This paper reviews state-of-the-art microfluidic technologies for environmental applications, such as on-site environmental monitoring and detection. Microdevices are extensively used in collecting environmental samples as a means to facilitate detection and quantification of targeted components with minimal quantities of samples. Likewise, microfluidic-inspired approaches for separation and treatment of contaminated water and air, such as the removal of heavy metals and waterborne pathogens from wastewater and carbon capture are also investigated.

Formation of reactive nitrogen species including peroxynitrite in physiological buffer exposed to cold atmospheric plasma

Cold Atmospheric Plasmas (CAPs) are increasingly used for biomedical applications, their various reactive components must be then better determined. We demonstrate that peroxynitrite (ONOO⁻) is effectively a major reactive species generated by CAPs.