Thermal cracking of waste printed wiring boards for mechanical recycling by using residual steam preprocessing

Mechanical waste-processing methods, which combine crushing and separation processes for the recovery of valuable materials, have been widely applied in waste printed wiring board (PWB) treatment. However, both the high impact toughness and the tensile and flexural strengths of whole PWB with a laminated structure result in great energy consumption and severe abrasion of the cutters during multi-level crushing. In addition, the high temperatures occurring in continual crushing probably cause the decomposition of the polymer matrix. A thermal-crack method using residual steam as the heating medium has been developed to pre-treat waste PWBs. This treatment reduces the mechanical strength in order to improve the recovery rate of valuable materials in subsequent mechanical recycling. The changes of the PWBs’ macro-mechanical properties were studied to evaluate thermal expansion impacts associated with changes in temperature, and the dynamic dislocation micro-structures were observed to identify the fracture mechanism. The results showed that thermal cracking with steam at the temperature of 500 K can effectively attenuate the mechanical properties of waste PWBs, by reducing the impact, tensile and flexural strengths respectively, by 59.2%, 49.3% and 51.4%, compared to untreated PWB. Thermal expansion can also facilitate the separation of copper from glass fiber by reducing peel resistance by 95.4% at 500 K. It was revealed that the flexural fracture was a transverse cracking caused by concentrated stress when the heating temperature was less than 500 K, and shifted to a vertical cracking after exceeding 500 K.     

水稻基因类型与生长环境对精米中砷积累的影响

砷(As)是动植物非必需的有毒类金属。水稻是对As有较强吸收和富集能力的大宗粮食作物,大米的食用是我国及东南亚地区人体暴露As的最主要途径,严重威胁人体健康。水稻籽粒中As的积累与水稻的基因型及众多环境因子密切相关,而且表现出复杂的交互作用。在浙江省选择了不同土壤砷背景值的5块水稻田,同时在嘉兴市农科院布置了人工添加As的盆栽实验,比较了20个水稻品种在这6个实验点的精米中As含量。结果表明,水稻基因型与环境及其相互作用对水稻籽粒中As的积累均有极显著影响,并且筛选得到了5个精米中As含量较低的基因型:秀水128、秀水09、秀水134、甬粳16和Y-05-8。     

土默川平原不同盐渍化土壤酶活性特征的研究

为探讨土默川平原不同盐渍化程度土壤酶活性的特征,选取轻度、中度、重度盐渍化土壤为对象,采用滴定法、比色法研究了0～40 cm土壤蔗糖酶、过氧化氢酶、脲酶、碱性磷酸酶4种酶活性不同季节的变化特征。结果表明:随盐渍化程度加剧,4种酶活性均呈现逐渐降低的趋势,蔗糖酶活性变化在0.65～36.55 mg·g-1、过氧化氢酶活性变化在2.76～3.35 mL·g-1、脲酶活性变化在0.003～0.018 mg·g-1、碱性磷酸酶活性变化在0.10～0.98 mg·g-1;土壤蔗糖酶、脲酶、碱性磷酸酶随土层加深酶活性降低,而过氧化氢酶活性表现为20～40 cm土层高于0～20 cm;不同盐渍化程度土壤酶活性的季节性变化趋势不同,过氧化氢酶和蔗糖酶活性的季节性变化呈先升高后下降的趋势,脲酶和碱性磷酸酶活性的季节性变化呈先升高后下降再升高再下降的趋势,碱性磷酸酶随时间的推移酶活性逐渐上升,且土壤酶活性出现峰值也不同,蔗糖酶和脲酶出现在8月,过氧化氢酶出现在7月,碱性磷酸酶出现在9月;不同盐渍化程度土壤酶活性在一个生长季内变化幅度表现为轻度>中度>重度盐渍化;不同盐渍化土壤碱性磷酸酶与脲酶、蔗糖酶活性呈极显著正相关,与过氧化氢酶活性呈极显著负相关,蔗糖酶与脲酶活性呈极显著正相关。因此,土默川平原不同盐渍化程度土壤酶活性及其季节性变化差异显著,土壤盐分含量影响着该地区土壤酶活性的变化。     

The effect of preparation conditions of Pt/Al2O3 on its catalytic performance for the H2-SCR in the presence of oxygen

Selective catalytic reduction of NO _x by H₂ in the presence of oxygen has been investigated over Pt/ Al₂O₃ catalysts pre-treated under different conditions. Catalyst preparation conditions exert significant influence on the catalytic performance, and the catalyst pre-treated by H₂ or H₂ then followed by O₂ is much more active than that pre-treated by air. The higher surface area and the presence of metallic Pt over Pt/Al₂O₃ pre-treated by H₂ or pretreated by H₂ then followed by O₂ can contribute to the formation of NO₂, which then promotes the reaction to proceed at low temperatures.     

NaCl和Na2SO4盐胁对铜绿微囊藻生长影响的对比研究

利用实验模拟研究了NaCl和Na2SO4两种盐对铜绿微囊藻Microcystis aeruginosa生长的影响。通过向M11培养液中添加不同浓度的NaCl和Na2SO4,设定0.5、1、2、4 g·L-14个盐度梯度,研究NaCl和Na2SO4两种盐对藻细胞密度及叶绿素a的影响。实验结果表明,铜绿微囊藻可耐受2 g·L-1的NaCl和Na2SO4盐度胁迫,当盐度超过2 g·L-1后对微囊藻的生长起明显抑制作用。Na2SO4对藻密度及叶绿素a的影响较NaCl更显著。研究显示盐度可能是抑制咸水湖泊蓝藻水华发生的重要影响因素之一,因此制定我国西北地区湖泊富营养化控制标准时,建议应考虑盐的主要种类及盐浓度的影响。     

2012年上半年我国南方地区极端阴雨天气评价——以湖南省为例

基于湖南省97个地面气象观测站观测资料,根据所选定的阴雨天气评价指标,运用数理统计方法,对2012年上半年湖南省阴雨天气特征及强度进行分析评估,结果表明,阴雨天气阶段性特征显著,属隆冬至初春历史罕见的寡照低温多雨事件与初春至初夏的极端多雨寡照事件的组合。同时根据社会调查数据对阴雨天气的影响展开多方位分析,得出低温阴雨天气不仅对农业造成了重大影响,而且对水利、交通、建筑业等多行业和人们身体健康也有较大影响,以此提出了应对极端天气气候事件的对策建议。     

Hydrogeochemical and Isotopic Investigations on Groundwater Salinization in the Datong Basin,Northern China1

Abstract: Analyses of major elements, environmental isotope ratios (δ¹⁸O, δ²H), and PHREEQC inverse modeling investigations were conducted to understand the processes controlling the salinization of groundwater within the Datong Basin. The hydrochemical results showed that groundwater with high total dissolved solid (TDS) concentrations was dominated by sodium bicarbonate (Na‐HCO₃), sodium chlorite (Na‐Cl), and sodium sulfate (Na‐SO₄) type waters, whereas low‐TDS groundwater from near mountain areas was dominated by calcium bicarbonate (Ca‐HCO₃) and magnesium bicarbonate (Mg‐HCO₃) type waters. The characterization of the major components of groundwater and PHREEQC inverse modeling indicated that the aluminosilicate hydrolysis, cation exchange, and dissolution of evaporites (halite, mirabilite, and gypsum) governed the salinization of groundwater within the Datong Basin. The environmental isotope (δ¹⁸O, δ²H) and Cl^?/Br^? ratios revealed the impact of fast vertical recharge by irrigation returns and salt‐flushing water on the groundwater salinization. According to the analyses of major hydrochemical components and PHREEQC inverse modeling, evaporite dissolution associated with irrigation and salt‐flushing practice was probably the dominant controlling factor for the groundwater salinization, especially in the central part of the basin. Therefore, groundwater pumping for irrigation and salt‐flushing should be controlled to protect groundwater quality in this area.     

Peracetic Acid Sanitation on Arugula Microgreens Contaminated with Surface-Attached and Internalized Tulane Virus and Rotavirus

Food and Environmental Virology - Hydroponic production of vegetables is becoming more common, especially in regions with unfavorable climate for year-round crop production. However, if viruses are...     

草木樨在汞胁迫下的遗传分化研究

以草木樨(Melilotus officinalis)种子为研究对象,将其暴露于0、20、40和60 mg·L-1浓度的氯化汞中进行萌发,并对存活的幼苗利用水平淀粉凝胶电泳进行等位酶分析.结果表明,草木樨在Me和Pgi基因座具有多态性,其平均的等位基因数为3.0基因座-1,杂合性为0.332 - 0.342,草木樨符合...     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.