Using Flory-Huggins phase diagrams as a pre-formulation tool for the production of amorphous solid dispersions; a comparison between hot-melt extrusion and spray drying

Objectives: Amorphous drug forms provide a useful method of enhancing the dissolution performance of poorly water-soluble drugs; however, they are inherently unstable. In this article, we have used Flory–Huggins theory to predict drug solubility and miscibility in polymer candidates, and used this information to compare spray drying and melt extrusion as processes to manufacture solid dispersions.
Method:  Solid dispersions were characterised using a combination of thermal (thermogravimetric analysis and differential scanning calorimetry) and spectroscopic (Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction methods. 
Key Findings: Spray drying permitted generation of amorphous solid dispersions to be produced across a wider drug concentration than melt extrusion. Melt extrusion provided sufficient energy for more intimate mixing to be achieved between drug and polymer, which may improve physical stability. It was also confirmed that stronger drug–polymer interactions might be generated through melt extrusion. Remixing and dissolution of recrystallised felodipine into the polymeric matrices did occur during the modulated differential scanning calorimetry analysis, but the complementary information provided from FTIR confirms that all freshly prepared spray-dried samples were amorphous with the existence of amorphous drug domains within high drug-loaded samples. 
Conclusion: Using temperature–composition phase diagrams to probe the relevance of temperature and drug composition in specific polymer candidates facilitates polymer screening for the purpose of formulating solid dispersions.

Multiple source quantum well model of porous silicon light emission

Based on photoluminescence, Fourier transform infrared spectroscopy, and atomic force microscopy results, a new light emitting model for porous silicon (multiple source quantum well model) is proposed.

The effect of niobium and tantalum on physicochemical and catalytic properties of silver and platinum catalysts based on MCF mesoporous cellular foams

MCF, NbMCF and TaMCF Mesostructured Cellular Foams were used as supports for platinum and silver (1 wt%). Metallic and bimetallic catalysts were prepared by grafting of metal species on APTMS (3-aminopropyltrimethoxysilane) and MPTMS (2-mercaptopropyltrimethoxysilane) functionalized supports. Characterizations by X-ray diffraction (XRD), ultraviolet–visible (UV–Vis) spectroscopy, X-ray photoelectron spectroscopy (XPS), X-ray fluorescence (XRF) spectroscopy, and in situ Fourier Transform Infrared (FTIR) spectroscopy allowed to monitor the oxidation state of metals and surface properties of the catalysts, in particular the formation of bimetallic phases and the strong metal–support interactions. It was evidenced that the functionalization agent (APTMS or MPTMS) influenced the metals dispersion, the type of bimetallic species and Nb/Ta interaction with Pt/Ag. Strong Nb–Ag interaction led to the reduction of niobium in the support and oxidation of silver. MPTMS interacted at first with Pt to form Pt–Ag ensembles highly active in CH3OH oxidation. The effect of Pt particle size and platinum–silver interaction on methanol oxidation was also considered. The nature of the functionalization agent strongly influenced the species formed on the surface during reaction with methanol and determined the catalytic activity and selectivity.

A comparison of gold nanoparticle surface co-functionalization approaches using Polyethylene Glycol (PEG) and the effect on stability, non-specific protein adsorption and internalization

To create clinically useful gold nanoparticle (AuNP) based cancer therapeutics it is necessary to co-functionalize the AuNP surface with a range of moieties; e.g. Polyethylene Glycol (PEG), peptides and drugs. AuNPs can be functionalized by creating either a mixed monolayer by attaching all the moieties directly to the surface using thiol chemistry, or by binding groups to the surface by means of a bifunctional polyethylene glycol (PEG) linker. The linker methodology has the potential to enhance bioavailability and the amount of functional agent that can be attached. While there is a large body of published work using both surface arrangements independently, the impact of attachment methodology on stability, non-specific protein adsorption and cellular uptake is not well understood, with no published studies directly comparing the two most frequently employed approaches. This paper compares the two methodologies by synthesizing and characterizing PEG and Receptor Mediated Endocytosis (RME) peptide co-functionalized AuNPs prepared using both the mixed monolayer and linker approaches. Successful attachment of both PEG and RME peptide using the two methods was confirmed using Dynamic Light Scattering, Fourier Transform Infrared Spectroscopy and gel electrophoresis. It was observed that while the 'as synthesized' citrate capped AuNPs agglomerated under physiological salt conditions, all the mixed monolayer and PEG linker capped samples remained stable at 1M NaCl, and were stable in PBS over extended periods. While it was noted that both functionalization methods inhibited non-specific protein attachment, the mixed monolayer samples did show some changes in gel electrophoresis migration profile after incubation with fetal calf serum. PEG renders the AuNP stable in-vivo however, studies with MDA-MB-231 and MCF 10A cell lines indicated that functionalization with PEG, blocks cellular uptake. It was observed that co-functionalization with RME peptide using both the mixed monolayer and PEG linker methods greatly enhanced cellular internalization compared to PEG capped AuNPs.

Generating Data Augmented Spectroscopic Data For Performance Enhancement

The application of chemometrics in food science has revolutionized the field by allowing the creation of models able to automate a broad range of applications such as food authenticity and food fraud detection. In order to create effective and general models able to address the complexity of real life problems, a vast amount of varied training samples are required. Training dataset has to cover all possible types of sample and instrument variability. However, acquiring a varied amount of samples is a time consuming and costly process, in which collecting samples representative of the real world variation is not always possible, specially in some application fields. To address this problem, a novel framework for the application of data augmentation techniques to spectroscopic data has been designed and implemented. This is a carefully designed pipeline of four complementary and independent blocks which can be finely tuned depending on the desired variance for enhancing model's robustness: a) blending spectra, b) changing baseline, c) shifting along x axis, and d) adding random noise.
This novel data augmentation solution has been tested in order to obtain highly efficient generalised classification model based on spectroscopic data. Fourier transform mid-infrared (FT-IR) spectroscopic data of eleven pure vegetable oils (106 admixtures) for the rapid identification of vegetable oil species in mixtures of oils have been used as a case study to demonstrate the influence of this pioneering approach in chemometrics, obtaining a 10% improvement in classification which is crucial in some applications of food adulteration.