Rapid Development of New Polymer Blends: The Optimal Selection of Materials and Blend Ratios

Chemo-metric engineering designs for deciphering the biodegradation of polycyclic aromatic hydrocarbons

Polycyclic aromatic hydrocarbons (PAHs) are non-polar organic compounds that are omnipresent in the environment and released due to anthropogenic activities through emissions and discharges. PAHs, being xenobiotic and exerts health impacts, thus they attract serious concern by the environmentalists. The stringent regulations and the need of sustainable development urges the hunt for a technically feasible and cost-effective wastewater treatment. Although the conventional physico-chemical treatment are widely preferred, they cause secondary pollution problems and demand subsequent treatment options. This comprehensive review intends to address the (a) different PAHs and their associated toxicity, (b) the remedial strategies, particularly biodegradation. The biological wastewater treatment techniques that involve microbial systems are highly influenced by the different physio-chemical and environmental parameters. Therefore, suitable optimization techniques are prerequisite for effective functioning of the biological treatment that sustains judiciously and interpreted in a lesser time. Here we have aimed to discuss (a) different chemo-metric tools involved in the design of experiments (DoE), (b) design equations and models, (c) tools for evaluating the model's adequacy and (d) plots for graphically interpreting the chemo-metric designs. However, to best of our knowledge, this is a first review to discuss the PAHs biodegradation that are tailored by chemo-metric designs. The associated challenges, available opportunities and techno-economic aspects of PAHs degradation using chemo-metric engineering designs are explained. Additionally, the review highlights how well these DoE tools can be suited for the sustainable socio-industrial sectors. Concomitantly, the futuristic scope and prospects to undertake new areas of research exploration were emphasized to unravel the least explored chemo-metric designs.

Poly[N-(2-chloroprop-2-en-1-yl)aniline]s: synthesis, polymer analogous reaction, and physicochemical properties

A series of new soluble N-substituted poly(β-haloallylanilines) were synthesized by oxidative polymerization. Based on these polymers and polyphosphoric acid, various functionalized polyindoles of a new type were obtained in high yields.

Compositional analysis of lignocellulosic biomass: conventional methodologies and future outlook

The composition and structural properties of lignocellulosic biomass have significant effects on its downstream conversion to fuels, biomaterials, and building-block chemicals. Specifically, the recalcitrance to modification and compositional variability of lignocellulose make it challenging to optimize and control the conditions under which the conversion takes place. Various characterization protocols have been developed over the past 150 years to elucidate the structural properties and compositional patterns that affect the processing of lignocellulose. Early characterization techniques were developed to estimate the relative digestibility and nutritional value of plant material after ingestion by ruminants and humans alike (e.g. dietary fiber). Over the years, these empirical techniques have evolved into statistical approaches that give a broader and more informative analysis of lignocellulose for conversion processes, to the point where an entire compositional and structural analysis of lignocellulosic biomass can be completed in minutes, rather than weeks. The use of modern spectroscopy and chemometric techniques has shown promise as a rapid and cost effective alternative to traditional empirical techniques. This review serves as an overview of the compositional analysis techniques that have been developed for lignocellulosic biomass in an effort to highlight the motivation and migration towards rapid, accurate, and cost-effective data-driven chemometric methods. These rapid analysis techniques can potentially be used to optimize future biorefinery unit operations, where large quantities of lignocellulose are continually processed into products of high value.

General procedure to aid the development of continuous pharmaceutical processes using multivariate statistical modeling - an industrial case study

Streamlining the manufacturing process has been recognized as a key issue to reduce production costs and improve safety in pharmaceutical manufacturing. Although data available from earlier developmental stages are often sparse and unstructured, they can be very useful to improve the understanding about the process under development. In this paper, a general procedure is proposed for the application of latent variable statistical methods to support the development of new continuous processes in the presence of limited experimental data. The proposed procedure is tested on an industrial case study concerning the development of a continuous line for the manufacturing of paracetamol tablets. The main driving forces acting on the process are identified and ranked according to their importance in explaining the variability in the available data. This improves the understanding about the process by elucidating how different active pharmaceutical ingredient pretreatments, different formulation modes and different settings on the processing units affect the overall operation as well as the properties of the intermediate and final products. The results can be used as a starting point to perform a comprehensive and science-based quality risk assessment that help to define a robust control strategy, possibly enhanced with the integration of a design space for the continuous process at a later stage.

De-risking pharmaceutical tablet manufacture through process understanding, latent variable modeling, and optimization technologies

In pharmaceutical tablet manufacturing processes, a major source of disturbance affecting drug product quality is the (lot-to-lot) variability of the incoming raw materials. A novel modeling and process optimization strategy that compensates for raw material variability is presented. The approach involves building partial least squares models that combine raw material attributes and tablet process parameters and relate these to final tablet attributes. The resulting models are used in an optimization framework to then find optimal process parameters which can satisfy all the desired requirements for the final tablet attributes, subject to the incoming raw material lots. In order to de-risk the potential (lot-to-lot) variability of raw materials on the drug product quality, the effect of raw material lot variability on the final tablet attributes was investigated using a raw material database containing a large number of lots. In this way, the raw material variability, optimal process parameter space and tablet attributes are correlated with each other and offer the opportunity of simulating a variety of changes in silico without actually performing experiments. The connectivity obtained between the three sources of variability (materials, parameters, attributes) can be considered a design space consistent with Quality by Design principles, which is defined by the ICH-Q8 guidance (USDA 2006). The effectiveness of the methodologies is illustrated through a common industrial tablet manufacturing case study.