Replacing Traditional Plastics with Biodegradable Plastics:Impact on Carbon Emissions

In recent years, a great deal of attention has been focused on the environmental impact of plastics, including the carbon emissions related to plastics, which has promoted the application of biodegradable plastics. Countries worldwide have shown high interest in replacing traditional plastics with biodegradable plastics. However, no systematic comparison has been conducted on the carbon emissions of biodegradable versus traditional plastic products. This study evaluates the carbon emissions of traditional and biodegradable plastic products (BPPs) over four stages and briefly discusses environmental and economic perspectives. Four scenarios—namely, the traditional method, chemical recycling, industrial composting, and anaerobic digestion—are considered for the disposal of waste biodegradable plastic product (WBBPs). The analysis takes China as a case study. The results show that the carbon emissions of 1000 traditional plastic products (plastic bags, lunch boxes, cups, etc.) were 52.09–150.36 carbon emissions equivalent of per kilogram (kg CO2eq), with the stage of plastic production contributing 50.71%–50.77%. In comparison, 1000 similar BPPs topped out at 21.06–56.86 kg CO2eq, approximately 13.53%–62.19% lower than traditional plastic products. The difference was mainly at the stages of plastic production and waste disposal, and the BPPs showed significant carbon reduction potential at the raw material acquisition stage. Waste disposal plays an important role in environmental impact, and composting and anaerobic digestion are considered to be preferable disposal methods for WBBPs. However, the high cost of biodegradable plastics is a challenge for their widespread use. This study has important reference significance for the sustainable development of the biodegradable plastics industry.</p

Correction to: Growth mechanism identification of sputtered single crystalline bismuth nanowire

The article listed above was initially published with incorrect copyright information

Growth mechanism identification of sputtered single crystalline bismuth nanowire

Abstract(#br)Single crystalline bismuth nanowire is recently considered as one of the most attractive low dimensional materials for the exploration of exotic higher-order topological properties. However, its growth mechanism by sputtering, which is regarded as one of the most cost-effective and simplest method, is still unrevealed. In this work, a bismuth nanowire growth model based on surface diffusion driven by chemical potential difference among crystal facets is proposed for sputtering method. The morphology evolution of bismuth nanowires is captured for the first time, and three corresponding growth stages are clearly discriminated. The possible self-catalyzed, stress-driven, and screw dislocation-driven nanowire growth mechanisms are precluded separately based on the theoretical and..

Tailoring the interfaces of silicon/carbon nanotube for high rate lithium-ion battery anodes

Abstract(#br)Micrometer-sized silicon powders, due to its high specific capacity, easy accessibility, and low cost, have been regarded as an attractive anode material for lithium-ion batteries. The severer mechanical instability and high inter-particle resistance during cycling, however, hinder its further application. In this work, a novel potholed micrometer-sized silicon powders (PMSi)/carbon nanotubes (CNT)/C electrode is proposed. The resulting three-dimensional (3D) conductive framework and multi-point contact network exhibit ideal structural stability and high-rate cycling property. Hence, the volume resistivity of PMSi/CNT/C (157 Ω m) is reduced significantly relative to traditional PMSi/commercial carbon nanotubes (CCT)/C composite (400 Ω m). By testing the fabricated half-cell LIB with the PMSi/CNT/C composite anode, high reversible specific capacity of 2533 mAh g −1 with a remarkable high initial coulombic efficiency of 89.07% and over 840 mA h g −1 for 1000 cycles at 2 A g −1 is measured. Even at the rate of 20 A g −1 , the PMSi/CNT/C electrode shows a capacity of 463 mAh g −1 . A full cell contained the PMSi/CNT/C anode and a LiFePO 4 /LiMn 2 O 4 cathode successfully ignites an LED array (∼1.5W), further demonstrating its outstanding electrical driving property

Enhanced reversible lithium storage in germanium nano-island coated 3D hexagonal bottle-like Si nanorod arrays

MOST of China [2009CB930704]; National Natural Science Foundation of China [61106118]; Science and Technology Project of Fujian Province of China [2013H0046]; Natural Science Foundation of Fujian Province of China [2011J01362]; Fundamental Research Funds for the Central Universities [2011121026]The rapid development of numerous microscale electronic devices, such as smart dust, micro or nano bio-sensors, medical implants and so on, has induced an urgent demand for integratable micro or nano battery supplies with high energy and power densities. In this work, 3D hexagonal bottle-like Si/Ge composite nanorod (NR) array electrodes with good uniformity and mechanical stability potentially used in micro or nano rechargeable Li-ion batteries (LIBs) were fabricated on Si substrates by a cost-effective, wafer scale and Si-compatible process. The optimized Ge nano-islands coated Si NR composite arrays as anode materials exhibit superior areal capacities and cycling performances by virtue of their favourable structural and improved conductivity features. The unique Si-based composite electrode in nanostructures can be technically and fundamentally employed to configurate all-solid-state Li-ion micro-batteries as on-chip power systems integrated into micro-electronic devices such as M/NEMS devices or autonomous wireless microsystems

Construction of an immunogenic cell death-based risk score prognosis model in breast cancer

Immunogenic cell death (ICD) is a form of regulated cell death that elicits immune response. Common inducers of ICD include cancer chemotherapy and radiation therapy. A better understanding of ICD might contribute to modify the current regimens of anti-cancer therapy, especially immunotherapy. This study aimed to identify ICD-related prognostic gene signatures in breast cancer (BC). An ICD-based gene prognostic signature was developed using Lasso-cox regression and Kaplan-Meier survival analysis based on datasets acquired from the Cancer Genome Atlas and Gene Expression Omnibus. A nomogram model was developed to predict the prognosis of BC patients. Gene Set Enrichment Analysis (GESA) and Gene Set Variation Analysis (GSVA) were used to explore the differentially expressed signaling pathways in high and low-risk groups. CIBERSORT and ESTIMATE algorithms were performed to investigate the difference of immune status in tumor microenvironment of different risk groups. Six genes (CALR, CLEC9A, BAX, TLR4, CXCR3, and PIK3CA) were selected for construction and validation of the prognosis model of BC based on public data. GSEA and GSVA analysis found that immune-related gene sets were enriched in low-risk group. Moreover, immune cell infiltration analysis showed that the immune features of the high-risk group were characterized by higher infiltration of tumor-associated macrophages and a lower proportion of CD8+ T cells, suggesting an immune evasive tumor microenvironment. We constructed and validated an ICD-based gene signature for predicting prognosis of breast cancer patients. Our model provides a tool with good discrimination and calibration abilities to predict the prognosis of BC, especially triple-negative breast cancer (TNBC)

Development of Transparent Mine Hydro-geological Modeling Software Based on Open CASCADE and Ordinary Kriging Algorithm

Based on Open CASCADE and Ordinary Kriging interpolation algorithm, a software development framework adding geostatistical interpolation algorithm to CAD geometry operation core for three-dimensional hydrogeological modeling is designed. Using three-dimensional graphics rendering of Open CASCADE, visual interaction, editing functions and geo-statistics interpolation function of Ordinary Kriging, we designed the hydrogeology modeling software for mine by taking Visual Studio as development tool, C++ and Python as development language, and SQLite as geological database, developed and Hydrogeo3D mine hydrogeology modeling software to realize the editable function of local details in the modeling process

Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime

Quality Assessment of Microseismic P-Phase Arrival Picks and Its Application of Source Location in Coal Mining

AbstractCorrectly identifying abnormal and false P-phase arrival picks (P-pick) in underground coal mining is essential to microseismic source location. Manual judgement and identification are time-consuming with the increasingly growing monitoring data. To eliminate the effects of false P-picks, a novel microseismic source location with weighted P-picks was proposed, and ten waveform parameters were selected to characterize the difference between two types of signals with usable and unusable P-picks. The discriminant analysis experiment has revealed that the prediction rate of unusable P-pick set increases dramatically with the sample size when the sample size is less than 2,000 and the prediction rates of unusable P-pick set are around 88% when the sample size is greater than 2,000, while the prediction rates of usable P-pick set are around 80%, which is little affected by the sample size. Considering the prediction rates of usable and unusable P-pick populations, the discrimination function with a sample size of 3,000 was selected to identify the usable and unusable P-picks. The identification rates of usable and unusable P-pick populations are up to 83.24% and 88.99%, respectively. The application of P-pick discriminant analysis model in source location was discussed. The location case and long-term result show that the P-pick discriminant model and its application in source location perform well