Textile Waste Fiber Regeneration via a Green Chemistry Approach: A Molecular Strategy for Sustainable Fashion

From Wiley via Jisc Publications RouterHistory: received 2021-07-06, rev-recd 2021-08-15, pub-electronic 2021-09-24, pub-print 2021-12-02Article version: VoRPublication status: PublishedFunder: EPSRC; Id: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/R00661X/1, EP/P025021/1, EP/P025498/1Abstract: Fast fashion, as a continuously growing part of the textile industry, is widely criticized for its excessive resource use and high generation of textiles. To reduce its environmental impacts, numerous efforts are focused on finding sustainable and eco‐friendly approaches to textile recycling. However, waste textiles and fibers are still mainly disposed of in landfills or by incineration after their service life and thereby pollute the natural environment, as there is still no effective strategy to separate natural fibers from chemical fibers. Herein, a green chemistry strategy is developed for the separation and regeneration of waste textiles at the molecular level. Cellulose/wool keratin composite fibers and multicomponent fibers are regenerated from waste textiles via a green chemical process. The strategy attempts to reduce the large amount of waste textiles generated by the fast‐developing fashion industry and provide a new source of fibers, which can also address the fossil fuel reserve shortages caused by chemical fiber industries and global food shortages caused by natural fiber production

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Feature-Based Digital Modulation Recognition Using Compressive Sampling

Compressive sensing theory can be applied to reconstruct the signal with far fewer measurements than what is usually considered necessary, while in many scenarios, such as spectrum detection and modulation recognition, we only expect to acquire useful characteristics rather than the original signals, where selecting the feature with sparsity becomes the main challenge. With the aim of digital modulation recognition, the paper mainly constructs two features which can be recovered directly from compressive samples. The two features are the spectrum of received data and its nonlinear transformation and the compositional feature of multiple high-order moments of the received data; both of them have desired sparsity required for reconstruction from subsamples. Recognition of multiple frequency shift keying, multiple phase shift keying, and multiple quadrature amplitude modulation are considered in our paper and implemented in a unified procedure. Simulation shows that the two identification features can work effectively in the digital modulation recognition, even at a relatively low signal-to-noise ratio

Low Complexity Cyclic Feature Recovery Based on Compressed Sampling

To extract statistic features of communication signal from compressive samples, such as cyclostationary property, full-scale signal reconstruction is not actually necessary or somehow expensive. However, direct reconstruction of cyclic feature may not be practical due to the relative high processing complexity. In this paper, we propose a new cyclic feature recovery approach based on the reconstruction of autocorrelation sequence from sub-Nyquist samples, which can reduce the computation complexity and memory consumption significantly, while the recovery performance remains well in the same compressive ratio. Through theoretical analyses and simulations, we conducted to show and verify our statements and conclusions

Textile Waste Fiber Regeneration via a Green Chemistry Approach: A Molecular Strategy for Sustainable Fashion

Fast fashion, as a continuously growing part of the textile industry, is widely criticized for its excessive resource use and high generation of textiles. To reduce its environmental impacts, numerous efforts are focused on finding sustainable and eco-friendly approaches to textile recycling. However, waste textiles and fibers are still mainly disposed of in landfills or by incineration after their service life and thereby pollute the natural environment, as there is still no effective strategy to separate natural fibers from chemical fibers. Herein, a green chemistry strategy is developed for the separation and regeneration of waste textiles at the molecular level. Cellulose/wool keratin composite fibers and multicomponent fibers are regenerated from waste textiles via a green chemical process. The strategy attempts to reduce the large amount of waste textiles generated by the fast-developing fashion industry and provide a new source of fibers, which can also address the fossil fuel reserve shortages caused by chemical fiber industries and global food shortages caused by natural fiber production