Quantitative analysis of binary polymorphs mixtures of fusidic acid by diffuse reflectance FTIR spectroscopy, diffuse reflectance FT-NIR spectroscopy, Raman spectroscopy and multivariate calibration

Identification and quantification techniques of polymorphic forms - A review

In the pharmaceutical industry, the unexpected appearance of crystalline forms could impact the therapeutic efficacy of an Active Pharmaceutical Ingredient (API). For quality control, a thorough qualitative and quantitative monitoring of pharmaceutical solid forms is essential to ensure the detection and the quantification of crystalline forms, wither different or with the same chemical composition (polymorphs) at a low detection level. The purpose of this paper was to review and highlight the importance of choosing adequate solid-state techniques for detection and quantification APIs that present polymorphism - based on limits of detection (LOD) and quantification (LOQ), pharmacopeias specifications, international guidelines and studies reported in the literature. To this study, the powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), Infrared and Raman spectroscopies and solid-state nuclear magnetic resonance (NMR) were the solid-state techniques analyzed. Additionally, the Argentine, Brazilian, British, European, International, Japanese, Mexican and the United States of America pharmacopeias were reviewed. Based on the analysis performed, the advantages and disadvantages of these techniques, as well as the LOD and LOQ values of APIs were reported. In comparison to these solid-state techniques, reference material used for identification analyses should be previously identified with the corresponding polymorph. Without this previous procedure, the patterns, the spectra, and DSC curves of the reference material can only be used to confirm the mixture of solid forms, not being able to specify which polymorphs are contained in the sample. A major advantage of PXRD is the use of the calculated diffraction patterns obtained from the Crystallographic Information Frameworks (CIFs) files which could be used as a reference pattern without any other information, assistance technique, or physical standards. Regarding the quantification aspect, different pharmacopeias suggest various methods such as the PXRD combining with Rietveld method, which can be used to obtain lower LOD values for minority phases in the mixture of different substances without the need for a calibration curve. Raman spectroscopy can detect polymorphs in small particles and solid-state NMR spectroscopy is a powerful technique for quantification not only crystalline but also crystalline-amorphous mixtures. Finally, this review intends to be a useful tool to control, with efficiency and accuracy, the polymorphism of APIs in pharmaceutical compounds.

Fabrication of Ni−MOF−74@PA−PEI for Radon Removal under Ambient Conditions

Radon is one of the 19 carcinogenic substances identified by the World Health Organization, posing a significant threat to human health and the environment. Properly removing radon under ambient conditions remains challenging. Compared with traditional radon−adsorbent materials such as activated carbon and zeolite, metal–organic framework (MOF) materials provide a high specific surface area, rich structure, and designability. However, MOF material powders demonstrate complications regarding practical use, such as easy accumulation, deactivation, and difficult recovery. Ni−MOF−74 was in situ grown on a porous polyacrylic acid (PA) spherical substrate via stepwise negative pressure impregnation. Ni−MOF−74 was structured as one−dimensional rod−shaped crystals (200–300 nm) in large−pore PA microspheres, whose porous structure increased the diffusion of radon gas. The radon adsorption coefficient of a Ni−MOF−74@PA−polyethyleneimine composite material was 0.49 L/g (293 K, relative humidity of 20%, air carrier). In comparison with pristine Ni−MOF−74 powder, our composite material exhibited enhanced adsorption and longer penetration time. The radon adsorption coefficient of the composite material was found to be from one to two orders of magnitude higher than that of zeolite and silica gel. The proposed material can be used for radon adsorption while overcoming the formation problem of MOF powders. Our preparation approach can provide a reference for the composite process of MOFs and polymers.

Fabrication of PA-PEI-MOF303(Al) by Stepwise Impregnation Layer-by-Layer Growth for Highly Efficient Removal of Ammonia

NH₃ is a typical alkaline gaseous pollutant widely derived from industrial production and poses great risks to humans and other biota. Metal-organic frameworks (MOFs) have excellent adsorption capacities relative to materials traditionally used to adsorb NH₃. However, in practice, applications of MOFs as adsorbents are restricted because of its powder form. We prepared a polyamide (PA) macroporous polyester substrate using an emulsion template method and modified the surface with polyethylenimine (PEI) to improve the MOF growth efficiency on the substrate. The difficulty of loading the MOF because of the fast nucleation rate inside the PA macroporous polyester substrate was solved using a stepwise impregnation layer-by-layer (LBL) growth method, and a PA-PEI-MOF303(Al) hierarchical pore composite that very efficiently adsorbed NH₃ was successfully prepared. The PA-PEI-MOF303(Al) adsorption capacity for NH₃ was 16.07 mmol·g^-1 at 298 K and 100 kPa, and the PA-PEI-MOF303(Al) could be regenerated repeatedly under vacuum at 423 K. The NH₃ adsorption mechanism was investigated by in situ Fourier transform infrared spectroscopy and by performing two-dimensional correlation analysis. Unlike for the MOF303(Al) powder, the formation of multi-site hydrogen bonds between Al-O-Al/C-OH, N-H, -OH, C=O, and NH₃ in PA-PEI-MOF303(Al) was found to be an important reason for efficient NH₃ adsorption. This study will provide a reference for the preparation of other MOF-polymer composites.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

A Highly Efficient and Stable Composite of Polyacrylate and Metal-Organic Framework Prepared by Interface Engineering for Direct Air Capture

We present a kilogram-scale experiment for assessing the prospects of a novel composite material of metal-organic framework (MOF) and polyacrylates (PA), namely NbOFFIVE-1-Ni@PA, for trace CO₂ capture. Through the interfacial enrichment of metal ions and organic ligands as well as heterogeneous crystallization, the sizes of microporous NbOFFIVE-1-Ni crystals are downsized to 200-400 nm and uniformly anchored on the macroporous surface of PA via interfacial coordination, forming a unique dual-framework structure. Specifically, the NbOFFIVE-1-Ni@PA composite with a loading of 45.8 wt % NbOFFIVE-1-Ni yields a superior CO₂ uptake (ca. 1.44 mol·kg^-1) compared to the pristine NbOFFIVE-1-Ni (ca. 1.30 mol·kg^-1) at 400 ppm and 298 K, indicating that the adsorption efficiency of NbOFFIVE-1-Ni has been raised by 2.42 times. Meanwhile, the time cost for realizing a complete adsorption/desorption cycle in a fluidized bed has been shortened to 25 min, and the working capacity (ca. 0.84 mol·kg^-1) declines only by 1.3% after 2000 cycles. The device is capable of harvesting 2.1 kg of CO₂ per kilogram of composite daily from simulated air with 50% relatively humidity (RH). To the best of our knowledge, the excellent adsorption/desorption performances of NbOFFIVE-1-Ni@PA position it as the most advantageous and practically applicable candidate for trace CO₂ capture.

Micro and Nanoplastics Identification: Classic Methods and Innovative Detection Techniques

Micro and nanoplastics are fragments with dimensions less than a millimeter invading all terrestrial and marine environments. They have become a major global environmental issue in recent decades and, indeed, recent scientific studies have highlighted the presence of these fragments all over the world even in environments that were thought to be unspoiled. Analysis of micro/nanoplastics in isolated samples from abiotic and biotic environmental matrices has become increasingly common. Hence, the need to find valid techniques to identify these micro and nano-sized particles. In this review, we discuss the current and potential identification methods used in microplastic analyses along with their advantages and limitations. We discuss the most suitable techniques currently available, from physical to chemical ones, as well as the challenges to enhance the existing methods and develop new ones. Microscopical techniques (i.e., dissect, polarized, fluorescence, scanning electron, and atomic force microscopy) are one of the most used identification methods for micro/nanoplastics, but they have the limitation to produce incomplete results in analyses of small particles. At present, the combination with chemical analysis (i.e., spectroscopy) overcome this limit together with recently introduced alternative approaches. For example, holographic imaging in microscope configuration images microplastics directly in unfiltered water, thus discriminating microplastics from diatoms and differentiates different sizes, shapes, and plastic types. The development of new analytical instruments coupled with each other or with conventional and innovative microscopy could solve the current problems in the identification of micro/nanoplastics.

Infrared spectroscopy technique for quantification of compounds in plant-based medicine and supplement

Quality control of plant-based medicine and supplements must be carried out to ensure uniformity in quality and safety in their use, resulting in the need for effective and accurate analytical methods. Infrared spectroscopy is a method of qualitative and quantitative analysis that is fast, time-saving, cost-effective,accurate, and nondestructive. This method has been applied for quantitative analysis of compounds in complex matrices such as plant-based medicine and supplements supported by chemometrics techniques. The success of infrared spectroscopy applications for quantitative analysis of phytochemicals and adulterants content in plant-based medicine and supplement can happen by several factors. This article highlights the effect of spectral preprocessing and variable selection on quantitative analysis of phytochemical and adulterant in plant-based medicine and supplements using infrared spectroscopy. Literature search was conducted with PubMed, Google Scholar, and Science Direct by selecting quantitative analysis research on plant-based medicines and supplements that utilize spectral preprocessing techniques and variable selection in processing data analysis. The preprocessing spectra and variables selection can affect the accuracy and precision of infrared spectroscopy methods. The variable selection can be done using the wavenumber point technique, the wavenumber interval, or a combination thereof. Variable selection is more commonly used for near-infrared data than for IR data. The optimization of the preprocessing spectra and variables selection technique will be useful in increasing the ability of infrared spectroscopy in predicting compound levels.

A rapid analytical method for the quantitative determination of the sugar in acarbose fermentation by infrared spectroscopy and chemometrics

In this article, a rapid analytical method for the quantitative determination of the glucose and maltose in the industrial acarbose fermentation was established by the combination application of infrared spectroscopy and chemometrics. The spectra of the 398 acarbose samples were collected by a portable infrared fast analyzer and the concentration of the glucose and maltose in the acarbose fermentation solution were determinate by high performance liquid chromatography (HPLC) as the referent database. Four spectral pretreatment methods, first derivative (FD), second derivative (SD), Savitzky-Golay (SG) convolution smoothing and mean center (MC) were employed to eliminate the optical interference from background and other noise information. The best result was obtained with FD+SG(21, 3)+MC method. The effects of different principal component numbers (PCs) on the parameters were also optimized. Two models of PLS and MLR, were used to predict the concentration of the glucose and maltose. The FD+SG(21, 3)+MC method was chosen as best method, with 12 PCs for glucose and 11 for maltose as optimized parameters. The PLS model was significantly better than the MLR model. Furthermore, both the predicted values and the reference values of glucose and maltose models showed superior linear relationship within the calibration range. The absolute errors of the predicted values and their corresponding reference values of glucose and maltose in the PLS model were within ±0.14 and ±0.35 confidence intervals, respectively. The prediction correct rate was 98.3%, which indicated that the prediction results of model were excellent.

Separation, characterization and identification of microplastics and nanoplastics in the environment

Microplastics (MPs) have globally been detected in aquatic and marine environments, which has raised scientific interests and public health concerns during the past decade. MPs are those polymeric particles with at least one dimension <5 mm. MPs possess complex physicochemical properties that vary their mobility, bioavailability and toxicity toward organisms and interactions with their surrounding pollutants. Similar to nanomaterials and nanoparticles, accurate and reliable detection and measurement of MPs or nanoplastics and their characteristics are important to warrant a comprehensive understanding of their environmental and ecological impacts. This review elaborates the principles and applications of diverse analytical instruments or techniques for separation, characterization and quantification of MPs in the environment. The strength and weakness of different instrumental methods in separation, morphological, physical classification, chemical characterization and quantification for MPs are critically compared and analyzed. There is a demand for standardized experimental procedures and characterization analysis due to the complex transformation, cross-contamination and heterogeneous properties of MPs in size and chemical compositions. Moreover, this review highlights emerging and promising characterization techniques that may have been overlooked by research communities to study MPs. The future research efforts may need to develop and implement new analytical tools and combinations of hyphenated technologies to complement respective limitations of detection and yield reliable characterization information for MPs. The goal of this critical review is to facilitate the research of plastic particles and pollutants in the environment and understanding of their environmental and human health effects.

Quantitative Monitoring the Anti-Solvent Crystallization and Storage Process for Nandrolone by Near-Infrared Spectroscopy

A novel hydrate (S_H2O) of nandrolone was prepared by anti-solvent methods. The crystallization processes with 2 schemes (A and B) were monitored by in-line near-infrared (NIR) spectroscopy. The amounts of S_H2O in powder samples obtained by the anti-solvent crystallization and storage process were quantified by NIR combined with chemometrics methods. In-line NIR spectra from 4500 to 8000 cm^-1 were chosen to capture physicochemical changes during the whole crystallization process. The combination of the principal component results with offline characterization (scanning electron microscopy, powder X-ray diffraction, NIR) data showed that both schemes yielded high purity S_H2O products, but the crystallization speed of scheme B was significantly accelerated. It was demonstrated that in-line NIR spectroscopy combined with principal component analysis can be very useful to monitor in real time and control the anti-solvent crystallization process. Moreover, the solubility and the solid-state transformation of nandrolone under different storage conditions were investigated. The apparent solubility of S_H2O was 2.19-2.44 times of Form I, and S_H2O was relatively stable when stored at a high relative humidity and temperature below 25°C.