Concentration effect of trace metals in Jordanian patients of urinary calculi

Due to the increase in the number of urinary calculi disease cases in Jordan, stone samples were collected from patients from various Jordanian hospitals (Princes Basma (PBH), King Abdullah University (KAUH), Al-Basheer (ABH) and Al-Mafraq (AMH)). This study concentrates on the effect of trace metals in patients of urinary calculi. Trace metals were detected in 110 urinary calculi samples using X-ray fluorescence (XRF) and atomic absorption spectroscopy (AAS) techniques. Of the calculi examined, 21 were pure calcium oxalate (CaOax), 29 were mixed calcium oxalate/uric acid, 23 were mixed calcium oxalate/phosphate (apatite), 25 were phosphate calculi (apatite/struvite), five were mixed calcium oxalate monohydrate/struvite, four were urate calculi (mixed ammonium acid urate/sodium acid urate) and three were pure cystine calculi. The concentration measurement of Ca and other trace metals levels has been found useful in understanding the mechanism of stone formation and in evaluating pathological factors. It has been found that Ca is the main constituent of the urinary calculi, especially those stones composed of calcium oxalate and calcium phosphate. The concentration of most of the trace metals that were analyzed was (Ca = 48.18, Na = 1.56, K = 0.9, Mg = 3.08, Fe = 1.17, Al = 0.49, Zn = 0.7, Cu = 0.19, Mn = 0.029, P = 10.35, S = 1.88, Sr = 0.306, Mo = 0.2, Cr = 0.146, Co = 0.05, Ni = 0.014)%. In conclusion, metals concentration in Jordanian patient’s urinary calculi samples was higher than its equivalents of other patients’. It has been noted that there is no concentration of toxic trace elements (like Li, V, Pb, Cd, and As). Some heavy metals, however, were detected Mo, Cr, Co and Ni as traces. P and S ions are present in few calculi stones as traces.     

污水厂二级出水溶解性有机物臭氧化特性研究

采用液相色谱荧光检测器将三维荧光图谱与凝胶色谱分子量分布相结合,对污水处理厂二级出水溶解性有机物(EfOM)的臭氧化反应特性进行了研究.结果表明臭氧会使得二级出水溶解性有机物三维荧光强度明显降低.HPLC-SEC的结果表明臭氧不会改变腐殖质类物质的分子量分布,但臭氧会与小分子蛋白类物质发生反应.根据制备液相的分析结果,小分子蛋白类物质中含有大量苯环及其他共轭结果物质,因此臭氧会优先与小分子蛋白类物质发生反应.同时,XPS分析结果表明臭氧在低投加量下就会与苯环结构反应,提高脂肪类饱和有机物含量,随着投加量进一步升高,含氧官能团(羰基,羧基)数量有所升高.     

融雪期城市河流中溶解性有机物的时空分布特征

以沈阳市新开河为研究对象,考察了河水中溶解性有机物(DOM)含量的月份变化,以及融雪期河水中DOM含量和特性的时空变化规律.利用XAD树脂将DOM分为5个部分:疏水性有机酸(HPO-A),疏水性中性有机物(HPO-N),过渡亲水性有机酸(TPI-A),过渡亲水性中性有机物(TPI-N)和亲水性有机物(HPI).结果表明,融雪期河水中的溶解性有机碳(DOC)浓度较高,HPO-N和TPI-N是融雪期河水中的主要DOM组分,并且DOM中的荧光物质主要为类芳香族蛋白质荧光物质和类腐植酸荧光物质.融雪期水体中的DOC、波长254nm处的紫外吸光度(UV-254)和三卤甲烷生成势(THMFP)呈先降低然后波动最后升高的变化趋势.特征紫外吸光度(SUVA)和三卤甲烷生成活性(STHMFP)与以上三者的变化规律相反.融雪期河水中的DOC受污水排放影响大,有排污口处的DOC含量明显大于其他区域.污水排放也使受纳水体中UV-254对THMFP的指示作用减弱.     

水体冻结过程中卤乙酸前体物在水-冰体系中的分配研究

通过室内模拟试验,研究了水体冻结过程中,水体中溶解性有机物(DOM)和卤乙酸前体物在水-冰体系中的分配规律.按照DOM在XAD树脂上的吸附特性将其分为5个部分:疏水性有机酸(HPO-A),疏水性中性有机物(HPO-N),过渡亲水性有机酸(TPI-A),过渡亲水性中性有机物(TPI-N)和亲水性有机物(HPI).结果表明:在水体冻结过程中,5种DOM组分在水相中的DOC浓度均随冷冻时间的增长而增加,呈现冷冻浓缩效应.与溶解性有机碳(DOC)所表征的整体有机物相比,5种DOM组分中的卤乙酸(HAAs)前体物更倾向于停留在水相中浓缩.在5种DOM组分中,HPI是主要的HAAs前体物.5种DOM组分在未冻结水中的UV-254与HAAFP均表现出一定相关性,其中HPO-A, TPI-A和HPI的UV-254与HAAFP达到极显著水平.然而在融冰水中,这5种DOM组分的UV-254与HAAFP的相关性均不显著.     

盐度冲击下MBR污泥SMP和EPS的三维荧光光谱解析

利用三维荧光(EEM)技术研究了不同盐度冲击下膜-生物反应器(MBR)污泥微生物溶解性产物(SMP)和胞外聚合物(EPS)的变化规律,分析不同盐度冲击下MBR污泥SMP和EPS的EEM图谱.结果表明,当冲击盐度大于2.5g/L时,SMP的EEM图谱中色氨酸荧光峰B(270nm,350nm)、类胡敏酸荧光峰C(375,475nm)、类富里酸荧光峰D(260,460nm)以及EPS的EEM图谱中色氨酸荧光峰B的荧光强度随冲击盐度的提升而增加,EPS的EEM图谱中类胡敏酸C峰的荧光强度则随冲击盐度的提升而下降.用平行因子分析(PARAFAC)方法确定EEM图谱中存在的4个组分,分别为类蛋白质组分C1(230/280,350nm),类胡敏酸组分C2(290/310,380nm)、C3(290/360,460nm)和C4(270/340,440nm);当冲击盐度大于2.5g/L时,SMP中C1、C3和C4组分的荧光强度与冲击盐度呈正相关,而EPS中C3和C4组分的荧光强度分别与冲击盐度呈正相关和负相关,其相关系数均≥0.90;SMP中类蛋白质荧光组分和类胡敏酸组分的荧光强度之和分别与测定的蛋白质含量和胡敏酸含量呈明显的正相关性,相关系数均＞0.93,可作为荧光组分强度定量计算的依据,而EPS中此相关性并不明显.     

基于BP神经网络的红外可燃气体探测器温度补偿研究

针对红外可燃气体探测器输出电压随温度变化,而又缺乏理论补偿公式的情况,提出采用BP神经网络对基于NDIR的可燃气体探测器进行温度补偿的方法,解决了以往温度补偿通过硬件补偿带来的探测器体积大、重量增加的弱点;与已有的软件补偿方法相比,BP神经网络方法通过对数据进行训练、学习获得温度补偿模型,得到了较好的结果,使得红外可燃气体探测器具有较宽的使用温度范围。     

A Method for Measuring Degradation of Individual Components in Multicomponent Biodegradable Plastics by Fourier Transform Infrared Spectrometry

A new chemometric method based on Beer's law was derived that uses peak ratios from Fourier transform infrared spectra of neat polymers and their composite plastics to quantify degradation of the individual polymers after biodegradation. The method affords direct measurement of polymer concentrations and weight losses without prior calibration against known composites. Unlike traditional chemometric methods, this method does not require sampling of as many or more different composites as the number of polymers in the composite being analyzed. When the neat polymer spectra are known, only two measurements, one before and one after biodegradation, are needed. A potentially major advance is that the method allows automation of analytical infrared wavelength selection by computer from all possible wavelength combinations. In this paper, the theoretical basis and derivation of the mathematical model for multicomponent systems is presented. The validity of the model was proved initially by applying the method to simulated two-polymer and three-polymer composites and finally by comparing test results with known samples of biodegradable composites prepared in the laboratory. Potential future development of the method for more challenging multicomponent plastics is discussed.     

废轮胎的热解及其产物分析

研究了废轮胎热解产生的三相 (气、液、固 )产率与裂解温度的关系 ,气相组成与温度的关系 ,以及氢氧化钠作为催化剂加入热解体系对三相产率和气体组成的影响 .实验表明 ,轮胎橡胶的热稳定性分为 :～ 200℃,200℃～300℃及300℃以上3个区域 .在200℃以上时 ,随着温度升高 ,固体产率减少 ,气体产率增加 ,液体产物的产率在 500℃左右出现一个峰值 .加入 4%NaOH作催化剂以后 ,在相同的温度下固态碳黑和液态油的产率均有所提高 ,而相应混合气的产率降低 .实验确定了所用混合胶粉主要为合成橡胶 .不同温度下热解所产生的液体产物的红外谱图及裂解色谱图表明 ,300℃时主要是助剂分解,400℃左右主要是聚异戊二烯(天然胶)开始裂解,600℃以上残留物开始裂解.     

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste. ● Feature selection methods were used to improve models’ prediction accuracy. ● The best model predicted C, H, and N contents with accuracy R ² ≥ 0.93, 0.87, 0.97. ● Some suitable models showed insensitivity to spectral noise. ● Under moisture interference, the models still had good prediction performance. Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R²) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.     

生物吸附剂ZL5-2对Cr(Ⅵ)的吸附机理

为测定环境样品中氯菊酯农药残留,用活泼酯法将氯菊酯半抗原(Py)与卵白蛋白(OVA)偶联,制备合成抗原Py-OVA作包被原,建立间接竞争酶联免疫吸附测定法.方阵滴定确定了抗血清最佳稀释度(1∶2 500),包被抗原的最适工作浓度0.45μg/mL,并建立了标准工作曲线.工作曲线表明在10～800μg/L浓度范围内呈良好的线性关系,回收率>97%.