Airbrush Formation of Liquid Crystal/Polymer Fibers

A homogeneous solution of a low‐molecular‐weight liquid crystal and a polymer spontaneously phase separates during airbrushing to form uniform fibers with a fluid liquid‐crystal core surrounded by a solid polymer sheath. This structure forms because it effectively minimizes the interfacial energy of the phase‐separated components while minimizing the elastic energy of the liquid‐crystal core. These fibers incorporate the sensitive stimuli response of liquid crystals while maintaining the structural integrity, flexibility, and large surface‐area‐to‐volume ratios inherent in fibers. We demonstrate the electro‐ and thermo‐optical response of the resulting fibers. They may find use as biological and chemical sensors. The resulting fibers have the potential to shape the future of flexible/wearable electronics and sensors.     

Multivariate dynamic modeling for Bayesian forecasting of business revenue

Forecasting enterprise-wide revenue is critical to many companies and presents several challenges and opportunities for significant business impact. This case study is based on model developments to address these challenges for forecasting in a large-scale retail company. Focused on multivariate revenue forecasting across collections of supermarkets and product categories, hierarchical dynamic models are natural: these are able to couple revenue streams in an integrated forecasting model, while allowing conditional decoupling to enable relevant and sensitive analysis together with scalable computation. Structured models exploit multi-scale modeling to cascade information on price and promotion activities as predictors relevant across categories and groups of stores. With a context-relevant focus on forecasting revenue 12 weeks ahead, the study highlights product categories that benefit from multi-scale information, defines insights into when, how, and why multivariate models improve forecast accuracy, and shows how cross-category dependencies can relate to promotion decisions in one category impacting others. Bayesian modeling developments underlying the case study are accessible in custom code for interested readers.     

The electro-optic properties of colloidal silica filled nematics

The optical and electro-optical properties of filled nematic liquid crystals, nematic systems with added colloidal silica nanoparticles (? 6%), have been studied. The macroscopic near IR birefringence of cells constructed from these materials was measured for wavelengths between 2 and 5 mum, and a wavelength independent value of 0.16 was obtained. The visible optical behaviour of cells formed with untreated ITO substrates using both filled nematic, and filled nematic and dichroic dyes was similar to those observed in polymer dispersed liquid crystals. At an electric field of 1–2 V μm ^-1, the cells were highly transmitting, while at low fields they were highly scattering. The effects of colloidal silica nanoparticle concentration, cell thickness, electric field and substrate preparation (rubbed polyimide versus no surface treatment) on the electro-optical behaviour of these cells were studied.     

Spatial condensation of trapped polaritons in graphene and semiconductor structures

The theoretical and experimental status of the Bose–Einstein Condensation (BEC) of trapped quantum well (QW) polaritons in a microcavity is presented. The results of recent experiments that have shown the possibility to create an in-plane harmonic potential trap for a two-dimensional (2D) exciton polaritons in a cavity are discussed. We report the theory of BEC and of the trapped QW exciton polaritons in a microcavity. In addition, we study the BEC of trapped magnetoexciton polaritons in a graphene layer (GL) embedded in an optical microcavity in high magnetic field. In both cases the polaritons are considered to be in a harmonic potential trap. We compare the theoretical results with the existing experiments and discuss the experimental observation of predicted phenomena.     

Synthesis of New Lipid‐Inspired Ionic Liquids by Thiol‐ene Chemistry: Profound Solvent Effect on Reaction Pathway

The synthesis of a series of new lipid‐inspired ionic liquids through thiol‐ene “click” reaction with a single‐step process. This synthesis offers considerable promise as an efficient and orthogonal method to construct structurally diverse imidazolium‐type ionic liquids with linear and branched cationic tails, as well as versatility in the placement of the sulfur heteroatom. Profound solvent effect in this ene reaction regioselectivity has been observed.     

Nanoporous carbons as promising novel methane adsorbents for natural gas technology

Nanoporous carbons were synthesized using furfuryl alcohol and sucrose as precursors and MCM-41 and mordenite as nanoporous templates.The produced nanoporous carbons were used as adsorbent for methane storage.The average pore diameter of the samples varied from 3.9 nm to 5.9 nm and the BET surface area varied from 320m2/g to 824m2/g.The volumetric adsorption experiments revealed that MCM-41 and sucrose had better performance compared with mordenite and furfuryl alcohol,correspondingly.Also,the effect of precursor to template ratio on the structure of nanoporous carbons and their adsorption capacities was investigated.The nanoporous carbon produced from MCM-41 mesoporous molecular sieve partially filled by sucrose shows the best methane adsorption capacity among the tested samples.     

Why Do Big Data and Machine Learning Entail the Fractional Dynamics?

Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the processing of complex signals, enhance the control of complex systems, increase the performance of optimization, and even extend the enabling of the potential for creativity. In this article, the authors discuss the fractional dynamics, FOT and rich fractional stochastic models. First, the use of fractional dynamics in big data analytics for quantifying big data variability stemming from the generation of complex systems is justified. Second, we show why fractional dynamics is needed in machine learning and optimal randomness when asking: “is there a more optimal way to optimize?”. Third, an optimal randomness case study for a stochastic configuration network (SCN) machine-learning method with heavy-tailed distributions is discussed. Finally, views on big data and (physics-informed) machine learning with fractional dynamics for future research are presented with concluding remarks.