Advanced ¹H Nuclear Magnetic Resonance (NMR) relaxometry and diffusometry methods and VIS-nearIR spectroscopy combined with pH, electrical conductivity (EC) and totally dissolved solids (TDSSs) measurements were used to assess the properties of wastewater collected from a chicken slaughterhouse in each step of the treatment process (wastewater before treatment, biologically treated wastewater, chemically treated wastewater and discharged wastewater) and from sludge. The ¹H NMR Carr–Purcell–Meiboom–Gill (CPMG) and Pulsed-Gradient-Stimulated-Echo (PGSE) decay curves recorded for all samples of wastewater were analyzed by inverse Laplace transform (ILT) to obtain the distributions of transverse relaxation times T₂ and diffusion coefficient D. The VIS-nearIR total absorbance, T₂-values, D-values, pH, EC and TDSS parameters were used for statistical analysis in principal component (PCA). The ¹H T₂-distributions measured for the slaughterhouse wastewater lie in two main regions reflecting the number of dissolved solids or the distribution of undissolved solids. The PCA analysis successfully differentiates between polluted and less polluted wastewaters and sludge. The wastewater treatment applied by the slaughterhouse is efficient. The recommended methods for wastewater monitoring are the NMR T₂- and D-distributions and EC, TDSSs and NMR-D diffusion coefficient. Finally, Machine Learning algorithms are used to provide prediction maps of wastewater treatment stage.

Biosolids from stabilized sludge present a high fertilization potential, due to their rich content of nutrients and organic matter. The intrinsic and subtle properties of such fertilizers may greatly influence the fertilization efficiency. In this sense, the utility, advantages and limitations of advanced characterization methods, for the investigation of structural and dynamic properties at the microscopic scale of slightly different formulations of fertilizers were assessed. For that, three formulas of organo-mineral fertilizers based on biosolids (V1, V2 and V3), having at least 2% N, 2% P₂O₅, and 2% K₂O, were characterized by advanced methods, such as ¹H NMR relaxometry, ¹H MAS and ¹³C CP-MAS NMR spectroscopy, ¹H double-quantum NMR and FT-IR spectroscopy. Advanced structural characterization was performed using SEM, EDX and X-ray diffraction. Four dynamical components were identified in the NMR T₂ distribution showing that the rigid component has a percentage larger than 90%, which explains the broad band of NMR spectra confirmed by the distributions of many components in residual dipolar coupling as were revealed by ¹H DQ-NMR measurements. SEM and EDX measurements helped the identification of components from crystalline-like X-ray diffraction patterns. To evaluate the release properties of organo-mineral fertilizers, dynamic measurements of classical electric conductivity and pH were performed by placing 0.25 g of the formulas (V1, V2 and V3) in 200 mL of distilled water. The content of N and P were quantified using specific reactants, combined with VIS-nearIR spectroscopy. Two release mechanisms were observed and characterized. It was found that V3 presents the smallest release velocity but releases the largest number of fertilizers.

Advanced 1H Nuclear Magnetic Resonance (NMR) relaxometry and diffusometry methods and VIS-nearIR spectroscopy combined with pH, electric conductivity (EC) and totally dissolved solids (TDS) measurements were used to assess the properties of wastewater collected from a chicken slaughterhouse in each step of the treatment process (wastewater before treatment, biologically treated wastewater, chemically treated wastewater and discharged wastewater) and from sludge. The 1H NMR Carr-Purcell-Meiboom-Gill (CPMG) and Pulsed-Gradient-Stimulated-Echo (PGSE) decay curves recorded for all samples of wastewater were analyzed by inverse Laplace transform (ILT) to obtain the distributions of transverse relaxation times T2 and diffusion coefficient D. The VIS-nearIR total absorbance, T2-values, D-values, pH, EC and TDS parameters were used for statistical analysis in principal component analysis (PCA). The 1H T2-distributions measured for the slaughterhouse wastewater lie in two main regions reflecting the amount of dissolved solids or the distribution of undissolved solids mobility. The PCA analysis successfully differentiates between polluted and less polluted types of wastewater and sludge. The wastewater treatment applied by the slaughterhouse is efficient. The recommended methods for wastewater monitoring are the NMR T2- and D-distributions and EC, TDS and NMR-D diffusion coefficient. Finally, Machine Learning AI-algorithms are used to provide prediction maps of wastewater treatment stage.

Electrospinning is a versatile technique used to manufacture nanofibers by applying an electric field to a polymer solution. This method has gained significant interest in the biomedical, pharmaceutical, and materials engineering fields due to its ability to produce porous structures with a high specific surface area, making it ideal for applications such as wound dressings, controlled drug delivery systems, and tissue engineering. The materials used in electrospinning play a crucial role in determining the final properties of the obtained nonwoven nanofibers. Among the most studied substances are chitosan, collagen, and fish-derived gelatin, which are biopolymers with high biocompatibility. These materials are especially used in the medical and pharmaceutical fields due to their bioactive properties. In combination with synthetic polymers such as polyethylene glycol (PEG) and polyvinyl alcohol (PVA), these biopolymers can form electrospun fibers with improved mechanical characteristics and enhanced structural stability. The characterization of these materials was performed using modern characterization techniques, such as one-dimensional (1D) proton NMR spectroscopy (¹H), for which the spin–spin relaxation time distributions T₂ were characterized. Additionally, two-dimensional (2D) measurements were conducted, for which EXSY T₂-T₂ and COSY T₁-T₂ exchange maps were obtained. The characterization was complemented with FT-IR spectra measurements, and the nanofiber morphology was observed using SEM. As a novelty, machine learning methods, including artificial neural networks (ANNs), were applied to characterize the local structural order of the produced nanofibers. In this study, it was shown that the nanofiber nonwoven materials made from PVA are characterized by a degree of order in the range of 0.27 to 0.61, which are more ordered than the nanofibers made from chitosan and fish gelatin, characterized by an order degree ranging from 0.051 to 0.312, where 0 represents the completely unordered network and 1 a fully ordered fabric.

Background/Objectives: Colorectal cancer (CRC) is one of the most common and deadly types of cancer. Compared with the classical histopathological approach, this study discusses the application of ¹H NMR and FT-IR techniques for the fast evaluation degree of healing of patients with CRC after surgical intervention. Methods: Native and deproteinized blood plasma collected from 10 patients with confirmed CRC and 20 healthy volunteers were analyzed using ¹H NMR T₂ distributions and FT-IR spectra measured for samples collected before and 7 days after surgery. The average FT-IR spectrum from 20 healthy volunteers is also presented. Principal component analysis (PCA) was performed on the FT-IR spectra. The results were used for further statistical analysis using receiver operating characteristic (ROC) and area under the curve (AUC) and to produce a series of prediction maps using a machine learning library. Results: Both experimental methods combined with analysis methods demonstrated that the native blood plasma samples can be better used to predict the CRC patients’ evolution 7 days after surgery. Three patients showed a significant evolution by ¹H NMR T₂ distribution, correlated to the observation of FT-IR–PCA analysis. Maps of medical state probability were generated using a trained machine learning-based ANN. Conclusions: The experimental measurements combined with an advanced statistical analysis and machine learning were successfully used and show that the healing process of patients with CRC is not linear, from the preoperative state to the state associated with healthy volunteers, but passes through a distinct healing state

Biosolids from stabilized sludge presents a high fertilization potential due to its rich content of nutrients and organic matter. Three formulas of organo-mineral fertilizers based on biosolids were manufactured having at least 2 % N, 2 % P2O5, and 2 % K2O and characterized by classical and advanced methods. The content of N and P in the final formula was measured using specific reactants combined with VIS-nearIR spectroscopy. Additionally, all three formulas were characterized by advanced 1H NMR relaxometry, double-quantum and FT-IR spectroscopy. Structural characterization was performed using SEM, EDX and X-ray diffraction. Four dynamical components were identified in the T2-distribution showing that the rigid component is into a percentage larger than 90 %. In order to evaluate the release properties of organo-mineral fertilizers dynamic measurements of electric conductivity and pH were performed, by placing 0.25 g of formulas (V1, V2 and V3) in 200 ml of distillate water. It was found that V3 present the smallest release velocity, but release the largest amount of fertilizers. Two release mechanisms were also observed.