Natural fibers and zinc hydroxystannate 3D microspheres based composite paper sheets for modern bendable energy storage application

For modern high-tech flexible energy storage devices, it becomes important to synthesize micro-/nanostructures as per the required shape and morphology with superior physical and electro-active characteristics. This work shares the fabrication and characterization of ZnSn(OH)₆ (Zinc hydroxystannate [ZHS]) prepared by facile microwave-assisted technique and furthermore converted into flexible sheets by employing lignocelluloses (LC) known as natural fibers, collected from Carica papaya leaf petiole as a substrate to provide the flexible matrix. X-ray diffraction measurements confirm the successful crystalline structure of ZHS. Scanning electron microscopy and transmission electron microscopy showed the solid spherical structure of ZHS microspheres. Fourier transform infrared spectrometry and Raman spectroscopy confirmed the composite formation of ZHS and LC-based composite sheets (ZHS/LC sheets). Electrochemical measurements that is, cyclic voltammetry (CV), Galvanostatic charge/discharge, and electrochemical impedance (EIS) spectroscopy revealed the electroactive behavior of ZHS/LC paper sheets as working electrode for energy storage applications. CV measurements revealed the specific capacitance of 100 F/g and EIS measurements confirmed the decrease in the resistance of LC fiber after the growth of ZHS microspheres. Presented flexible ZHS based paper sheets will be highly feasible for the modern bendable/flexible/disposable energy storage applications.     

Sol-Gel-Derived Ordered Mesoporous High Entropy Spinel Ferrites and Assessment of Their Photoelectrochemical and Electrocatalytic Water Splitting Performance

The novel material class of high entropy oxides with their unique and unexpected physicochemical properties is a candidate for energy applications. Herein, it is reported for the first time about the physico- and (photo-) electrochemical properties of ordered mesoporous (CoNiCuZnMg)Fe₂O₄ thin films synthesized by a soft-templating and dip-coating approach. The A-site high entropy ferrites (HEF) are composed of periodically ordered mesopores building a highly accessible inorganic nanoarchitecture with large specific surface areas. The mesoporous spinel HEF thin films are found to be phase-pure and crack-free on the meso- and macroscale. The formation of the spinel structure hosting six distinct cations is verified by X-ray-based characterization techniques. Photoelectron spectroscopy gives insight into the chemical state of the implemented transition metals supporting the structural characterization data. Applied as photoanode for photoelectrochemical water splitting, the HEFs are photostable over several hours but show only low photoconductivity owing to fast surface recombination, as evidenced by intensity-modulated photocurrent spectroscopy. When applied as oxygen evolution reaction electrocatalyst, the HEF thin films possess overpotentials of 420 mV at 10 mA cm⁻² in 1 m KOH. The results imply that the increase of the compositional disorder enhances the electronic transport properties, which are beneficial for both energy applications.     

A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches.