Dioxin emission prediction from a full-scale municipal solid waste incinerator: Deep learning model in time-series input.

The immeasurability of real-time dioxin emissions is the principal limitation to controlling and reducing dioxin emissions in municipal solid waste incineration (MSWI). Existing methods for dioxin emissions prediction are based on machine learning with inadequate dioxin datasets. In this study, the deep learning models are trained through larger online dioxin emissions data from a waste incinerator to predict real-time dioxin emissions. First, data are collected and the operating data are preprocessed. Then, the dioxin emission prediction performance of the machine learning and deep learning models, including long short-term memory (LSTM) and convolutional neural networks (CNN), with normal input and time-series input are compared. We evaluate the applicability of each model and find that the performance of the deep learning models (LSTM and CNN) has improved by 36.5% and 30.4%, respectively, in terms of the mean square error (MSE) with the time-series input. Moreover, through feature analysis, we find that temperature, airflow, and time dimension are considerable for dioxin prediction. The results are meaningful for optimizing the control of dioxins from MSWI.

Cellulose-Based Photothermal Coating: A Sustainable Solution for Seed Protection and Long-Term Grain Storage.

High-output modern agriculture based on synthetic chemicals (biocides, pesticides, and fertilizers) feeds the growing global population. To completely abandon the use of pesticides and fertilizers will undoubtedly cause a severe food crisis worldwide, and sustainable alternative solutions are urgently demanded to stop biocides and fertilizers overuse. Herein, a versatile and green strategy is proposed for seed protection and long-term storage of grains using a cellulose-based photothermal coating (PDA NPs@Cell-N+) that consists of photothermal polydopamine nanoparticles (PDA NPs) and a positive-charged cellulose derivative (Cell-N+) to eradicate seed-borne bacteria and fungi simply under infrared irradiation. In vitro and in vivo assays and the seedling-stage phenotypes of mung bean (Vigna radiata) suggest that pathogenic microbes, including the tough Aspergillus flavus (inhibition ratio >99%), can be efficiently eliminated by photothermal therapy. Thus, the seed-borne diseases of mung beans can finally be prevented. Owing to excellent solubility and biocompatibility, the PDA NPs@Cell-N+ coating can be washed off and recycled without food safety concerns. PDA NPs@Cell-N+ can be a nature-based solution for seed protection and long-term grain storage.

Cellulose-Based Cryogel Microspheres with Nanoporous and Controllable Wrinkled Morphologies for Rapid Hemostasis.

First-aid hemostatic agents for acute bleeding can save lives in emergency situations. However, rapid hemostasis remains challenging when uncontrolled hemorrhage occurs on lethal noncompressible and irregular wounds. Herein, cellulose-based cryogel microspheres with deliberately customized micromorphologies for ultrafast water transportation and diffusion, including the shark skin riblet-inspired wrinkled surface with low fluid drag and the hydrophilic nanoporous 3D networks, are developed to deal with the acute noncompressible bleeding within seconds. These cryogel microspheres can rapidly absorb a large amount of blood over 6 times their own weight in 10 s and form a robust barrier to seal a bleeding wound without applying pressure. Remarkably, massive bleeding from a cardiac penetrating hole is effectively stopped using the microspheres within 20 s and no blood leakage is observed after 30 min. Additionally, these microspheres could be readily removed without rebleeding and capillary thrombus, which is highly favorable to rapid hemostasis in emergency rescue.