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Petroleum-based plastics (PBP) with different properties have been developed to suit various needs of modern lives. Nevertheless, these well-developed properties also present the double-edged sword effect that significantly threatens the sustainability of the environment. This work focuses on the impact of microbial cultivating conditions (the elem...
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Secondary infections contribute significantly to covid-19 mortality but host and microbial factors driving this sequel remain poorly understood. We performed an autopsy study of 20 covid-19 cases and 14 controls from the first pandemic wave. Autopsies combined with microbial cultivation and deep RNA sequencing (RNAseq) allowed us to define major or...
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... Then, the supervised machine learning algorithm that were developed from our previous work was employed to obtain the optimal condition. The detailed settings for data training using rectified linear activation function (ReLU) in TensorFlow could be found from our previously reported works (Wang et al., 2021b;Liu et al., 2021;Sun et al., 2022). For the sake of better illustration, this ternary system is plot in a binary 2D diagram. ...
This paper reports an enhanced approach of thermal reduction of phosphogypsum (PG) in the presence of sodium
chloride (NaCl) in its molten phase. The thermodynamic together with in-situ thermogravimetric-Fourier
transform infrared spectroscopy (TG-FTIR) result indicates the yield of CaS (solid) at the investigated tempera
ture range (800–850 ◦C). Addition of NaCl not only appreciably reduces the activation energy of this reaction
(800–850 ◦C) on average from 315 to 175 (kJ⋅mol 1) materially, but also significantly improve the conversion
from CaSO4 to CaS. Additionally, the NaCl based waste salt (NaCl-WS) containing organic compounds (0.21 wt
%) was found to produce similar results when compared with the pure NaCl addition at investigated temperature
range. The optimal conditions via parametric optimization: i.e., 800–850 ◦C, 15 wt% (NaCl-WS), and 6 (ratio of
PG/C (carbon) wt/wt), were determined. The mechanistic study together with using density functional theories
(DFT) shows that the synergistic effect by NaCl addition was caused by the coupled-vacancy diffusion mechanism
when NaCl melts at high temperature and C atom shows proximity to oxygen (O) atom from the SO4 2- cluster in
the CaSO4 crystallite structure. The evaluation of the process shows appealing advantages over the conventional
PG thermal reduction. By carefully manipulating temperature range together with the NaCl-WS addition, the
proposed new process simultaneously realizes PG thermal reduction and environmental utilization of NaCl-WS.
... To avoid drying problem, the learning rate is arbitrarily set at a relatively lower rate (0.0001). To consistently compare with the results from our previously published works using deep learning [88][89][90], the leaky ReLU is not used in this work, let alone the deployment of leaky ReLU would appreciably reduce the overall performance of ML. Ever since its first release in 2017, TensorFlow (Google Brain's second-generation system), has evolved as an important role player in ML, due to its extraordinary flexible architecture allowing for the easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. ...
This review focuses on the parametric impacts upon conversion and selectivity during CO2 hydrogenation via
Fischer-Tropsch (FT) synthesis using iron-based catalyst to provide quantitative evaluation. Using all collected
data from reported literatures as training dataset via artificial neural networks (ANNs) in TensorFlow, three
categorized parameters (namely: operational, catalyst informatic and mass transfer) were deployed to assess
their impacts upon conversions (CO2) and selectivity. The lump kinetic power expressions among literature
reports were compared, and the best fit model is the one that was proposed by this work without arbitrarily
assuming power values of individual partial pressure (CO and H2). More than five sets of binary parameters were
systematically investigated to find out corresponding evolving patterns in conversion and selectivity. Aided by
machine learning, tailoring product distributions based on specific selectivity or conversion for optimization
purpose is practically achievable by deploying the predictions generated from ANNs in this work
... Surface-sterilized PLA films (2 × 2.5 cm 2 ), at an initial concentration of ≈6.9 mg/mL (8 PLA films in 50 mL Erlenmeyer flask containing 25 mL media), were added to Degradation-Basal Media (DBM, pH 7.0) composed of KH 2 PO 4 0.7 g/L, K 2 HPO 4 1.4 g/L, NaNO 3 8.0 g/L, NH 4 NO 3 3.0 g/L, NaCl 0.01 g/L, MgSO 4 ⋅7H 2 O 0.7 g/L, CaCl 2 ⋅2H 2 O 0.01 g/L, FeSO 4 ⋅7H 2 O 0.005 g/L, MnSO 4 .H 2 O 0.005 g/L, yeast extract 0.005 g/L, gelatin 3 g/L, and 0.05 mL/L of trace elements consisting of CuSO 4 .H 2 O 0.5 g/L, MoNa 2 O 4 ⋅2H 2 O 0.06 g/L, and ZnSO 4 ⋅7H 2 O 0.5 g/L [5,7,69]. To prepare bacterial inoculum, all four bacterial strains were separately cultured in tryptic soy broth, and after reaching the late logarithmic growth phase, were centrifuged at 8000 rpm for 10 min (4 • C). ...
Biodegradation of polylactic acid (PLA) in the natural environment is usually slow. This study aims to develop a bacterial consortium that biodegrades semi-crystalline PLA films with high molecular weight, at an ambient temperature of 30 °C. PLA-degrading bacteria were enriched from polymeric wastes. Proteobacteria, Bacteroidia and Actinobacteria relative abundances were high during the PLA-enrichment process. Four isolated bacteria, Nocardioides zeae EA12, Stenotrophomonas pavanii EA33, Gordonia desulfuricans EA63, and Chitinophaga jiangningensis EA02, which synthesized PLA degradative enzymes such as protease, esterase and lipase were mixed as consortium EAc. PLA films inoculated with EAc showed biofilm formation (10.54 log CFU/cm2), molecular weight (Mv) reduction (61%), and weight loss (9.68%) after 35 days. The extent of biodegradation was significantly higher than that by individual strains. Decrease in PLA Mv fitted the first order kinetics with a rate constant of 0.028 d−1. UV spectroscopy of biodegraded PLA suggested the carbonyl bond cleavage, while substantial chain scissions in the ester backbone of PLA were detected by ATR-FTIR. The rapid PLA biodegradation was due to the synthesis of diverse hydrolases and increase in pH, which led to enzymatic and alkaline hydrolysis. Consequently, EAc consortium could be applied in general solid waste treatment facilities for PLA degradation.
Energy crisis and environmental pollution have become the bottleneck of human sustainable development. Therefore, there is an urgent need to develop new catalysts for energy production and environmental remediation. Due to the high cost caused by blind screening and limited valuable computing resources, the traditional experimental methods and theoretical calculations are difficult to meet with the requirements. In the past decades, computer science has made great progress, especially in the field of machine learning (ML). As a new research paradigm, ML greatly accelerates the theoretical calculation methods represented by first principal calculation and molecular dynamics, and establish the physical picture of heterogeneous catalytic processes for energy and environment. This review firstly summarized the general research paradigms of ML in the discovery of catalysts. Then, the latest progresses of ML in light-, electricity- and enzyme-mediated heterogeneous catalysis were reviewed from the perspective of catalytic performance, operating conditions and reaction mechanism. The general guidelines of ML for heterogeneous catalysis were proposed. Finally, the existing problems and future development trend of ML in heterogeneous catalysis mediated by light, electricity and enzyme were summarized. We highly expect that this review will facilitate the interaction between ML and heterogeneous catalysis, and illuminate the development prospect of heterogeneous catalysis.
