Nanomaterials for electrochemical detection of pollutants in water: A review

The survival of living beings, including humanity, depends on a continuous supply of clean water. However, due to the development of industry, agriculture, and population growth, an increasing number of wastewaters is discarded, and the negative effects of such actions are clear. The first step in solving this situation is the collection and monitoring of pollutants in water bodies to subsequently facilitate their treatment. Nonetheless, traditional sensing techniques are typically laboratory-based, leading to potential diminishment in analysis quality. In this paper, the most recent developments in micro- and nano-electrochemical devices for pollutant detection in wastewater are reviewed. The devices reviewed are based on a variety of electrodes and the sensing of three different categories of pollutants: nutrients and phenolic compounds, heavy metals, and organic matter. From these electrodes, Cu, Co, and Bi showed promise as versatile materials to detect a grand variety of contaminants. Also, the most commonly used material is glassy carbon, present in the detection of all reviewed analytes.     

Alkaline oxygen evolution: exploring synergy between fcc and hcp cobalt nanoparticles entrapped in N-doped graphene

Herein, we report a Mott-Schottky catalyst by entrapping cobalt nanoparticles inside the N-doped graphene shell (Co@NC). The Co@NC delivered excellent oxygen evolution activity with an overpotential of merely 248 mV at a current density of 10 mA cm^–2 with promising long-term stability. The importance of Co encapsulated in NC has further been demonstrated by synthesizing Co nanoparticles without NC shell. The synergy between the hexagonal close-packed (hcp) and face-centered cubic (fcc) Co plays a major role to improve the OER activity, whereas the NC shell optimizes the electronic structure, improves the electron conductivity, and offers a large number of active sites in Co@NC. The density functional theory calculations have revealed that the hcp Co has a dominant role in the surface reaction of electrocatalytic oxygen evolution, whereas the fcc phase induces the built-in electric field at the interfaces with N-doped graphene to accelerate the H⁺ ion transport.     

Sentinel-2卫星的多光谱轻量级船舶目标检测算法

近年来深度卷积神经网络在可见光船舶检测方面取得了显著的进展，然而，大多数相关研究是通过改进大型的网络结构来提高检测性能，因此加大了对更高计算机性能的需求。此外，可见光图像难以在云、雾、海杂波、黑夜等复杂场景检测到船舶。针对以上问题，提出了一种融合红（red, R）、绿（green, G）、蓝（blue, B）和近红外（NIR）4个波段光谱信息的由粗到精细的轻量型船舶检测算法。与现有的方法中根据光谱特性利用水体检测算法提取水体区域不同之处是该算法是利用改进的水体检测算法来提取船舶候选区域。为获取更准确的候选区域，对船舶、厚云、薄云、平静海面、杂波海面5种场景中4个波段的像素值进行了统计分析，选取近红外大于阈值作为辅助判断，并以其中心点获取候选区域32×32大小的切片，并对切片进行非极大值抑制，由此获得了船舶粗检测结果。随后构建了轻量级LSGFNet网络对船舶候选区域切片进行精细识别。构建的网络融合了1×1卷积提取的波谱特征与3×3的提取几何特征，为防止光谱特征与几何特征的信息在融合时“信息不流通”，在LSGFNet网络中引入了ShuffleNet中的通道打乱机制，并减小了模型结构，与典型的轻量级网络相比具有更好的效果且模型较小。最后，利用Sentinel-2卫星多光谱10 m分辨率数据构建了512×512大小的1 120组数据进行粗检测，以及32×32大小的6 014组数据进行精细网络训练，其中候选区域粗提取的查全率为98.99%，精细识别网络精确度为96.04%，不同场景下的平均精确度为92.98%。实验表明该算法在抑制云层、海浪杂波等干扰的复杂背景下具有较高的检测效率，且训练时间短、计算机性能需求低。     

Field‐amplified sample injection combined with capillary electrophoresis for the simultaneous determination of five chlorophenols in water samples

A sensitive method of CZE‐ultraviolet (UV) detection based on the on‐line preconcentration strategy of field‐amplified sample injection (FASI) was developed for the simultaneous determination of five kinds of chlorophenols (CPs) namely 4‐chlorophenol (4‐CP), 2‐chlorophenol (2‐CP), 2,4‐dichlorophenol (2,4‐DCP), 2,4,6‐trichlorophenol (2,4,6‐TCP), and 2,6‐dichlorophenol (2,6‐DCP) in water samples. Several parameters affecting CZE and FASI conditions were systematically investigated. Under the optimal conditions, sensitivity enhancement factors for 4‐CP, 2‐CP, 2,4‐DCP, 2,4,6‐TCP, and 2,6‐DCP were 9, 27, 35, 43, and 43 folds, respectively, compared with the direct CZE, and the baseline separation was achieved within 5 min. Then, the developed FASI‐CZE‐UV method was applied to tap and lake water samples for the five CPs determination. The LODs (S/N = 3) were 0.0018–0.019 µg/mL and 0.0089–0.029 µg/mL in tap water and lake water, respectively. The values of LOQs in tap water (0.006–0.0074 µg/mL) were much lower than the maximum permissible concentrations of 2,4,6‐TCP, 2,4‐DCP, and 2‐CP in drinking water stipulated by World Health Organization (WHO) namely 0.3, 0.04, and 0.01 µg/mL, respectively, and thereby the method was suitable to detect the CPs according to WHO guidelines. Furthermore, the method attained high recoveries in the range of 83.0–119.0% at three spiking levels of five CPs in the two types of water samples, with relative standard deviations of 0.37–8.58%. The developed method was proved to be a simple, sensitive, highly automated, and efficient alternative to CPs determination in real water samples.     

工矿业城市区域水质参数高光谱定量反演

工矿业城市区域易受工业活动、矿产开采影响，使其水环境遭受不同程度的破坏，以至于水体污染问题突出。当前常规水质监测主要采用“以点代面”的工作方式进行野外采样及其室内化验分析，然而环境复杂多变，空间差异大，导致调查点代表性受限，整体精度不高，效率低下，更难以实现区域性动态监测。以因矿兴市的矿业重镇湖北黄石大冶市为研究区，同步开展无人机高光谱航飞、地面光谱测量和水体样品采集测试，分别获得具有49个波段的高光谱影像数据和光谱分辨率为1 nm的水体光谱曲线，其中影像数据波谱范围为505~890 nm，光谱分辨率为7.78 nm，空间分辨率为30 cm。对获取的高光谱影像和光谱数据剔除异常值、光谱定标、辐射校正等预处理后，对比分析研究区内水体的不同光谱吸收、反射及光谱曲线形态特征信息，从而提取出高光谱影像和测量光谱的反射光谱曲线形态特征、去包络线后光谱曲线形态特征、三阶求导后光谱曲线形态特征和光谱四值编码共四大类25个光谱特征。采用皮尔森相关系数分析样品水质参数与光谱特征之间的相关性，以此筛选出存在显著相关的水质参数与光谱特征。在此基础上，采用逐步回归分析方法筛选出最大反射率及其波长位置、对称度、光谱编码Ⅲ、三阶导最大及最小值等光谱特征作为模型变量，构建出水质参数的多元线性反演模型，并对模型进行F检验和t检验。将检验后的反演模型推广运用于研究区内高光谱影像，获得尾矿库、河流、湖泊等典型区域的水质参数反演结果，从而实现“由点到面”水质参数信息的快速获取。结果显示水质参数pH、硬度（Ca2++Mg2+）、钾与氯离子比值（K+/Cl-）、镁与碱度比值[Mg2+/(HCO3-+CO2-₃)]的反演精度较高，其pH的判定系数R2最小为0.669，镁与碱度比值的判定系数R2最大为0.895，相对均方根误差均小于28%；而总溶解固体（TDS）反演精度较低，其判定系数R2仅为0.463，相对均方根误差达36.762%。提出了一种基于光谱曲线形态特征的高光谱遥感水质参数定量反演模型方法，实现了pH值、硬度、镁离子与碱度之比等水质参数的高光谱定量反演，为区域水环境动态监测提供了新方法。     

智能手机的主要叶类蔬菜品质和新鲜度指标的光谱检测

蔬菜品质和新鲜度的高低不仅影响食用时的口感，而且营养程度也不一样。作为蔬菜品质和新鲜度重要参考指标之一的叶绿素和含水量的检测，已经越来越受到国内外学者的重视。相比于传统的肉眼目视判断的检验方法，可见-近红外光谱分析具有快速高效、无损、非接触等独特的优势，更加适合蔬菜的实时检测。目前相关研究主要集中在生长中植被叶绿素和含水量的反演，对市场上成品蔬菜的研究较少，或者研究对象单一，缺乏市场普适性。此外，光谱数据的获取需要专业的光谱仪采集，费时费力，各种生理生化指标的研究离实用化还有很长的距离。为了与实际相结合，基于智能手机光谱系统（SCSS)建立了快速、准确、普适性强的反演蔬菜叶绿素和含水量的模型，并通过地面光谱仪SVC数据验证了该系统的可靠性。选取市场典型的五种蔬菜（菠菜、小油菜、油麦菜、生菜和娃娃菜）作为实验样本，分别进行常温保存和冷藏保存来模拟现实中菜市场和超市的蔬菜储存环境。每隔24 h进行一次数据采集。对获取的原始光谱数据进行波段选择和小波变换去噪的预处理。构建蔬菜叶绿素反演指数(VCRI_{（m, n）})和蔬菜含水量反演指数(VWRI_{(i, j)})，分别提取该两个指数与叶绿素和含水量实测值的相关系数R作为权重系数，最终建立了叶绿素和含水量的加权平均反演模型。实验结果表明，SVC仪器和SCSS两者数据针对蔬菜叶绿素和含水量的敏感波段基本一致，叶绿素反演的敏感波段在730~980 nm之间，反演精度R2分别为0.863和0.808 1，标准差为8.679 5和8.892 5；含水量反演的敏感波段在水汽吸收波段950~1 000 nm之间，反演精度R2分别为0.742 9和0.712 9，标准差为8.789 9%和8.861 4%。SVC实验数据跟SCSS实验数据结果十分接近，验证了新型智能手机光谱系统实时监测蔬菜叶绿素和含水量的有效性。智能手机光谱系统具有体积小、价格便宜的优势，结合网络云端服务和实时数据反馈的特点，能够实现蔬菜品质和新鲜度指标的智能检测，让光谱分析真正应用于人们日常生活中。     

CdS quantum dot sensitized p-type NiO as photocathode with integrated cobaloxime in photoelectrochemical cell for water splitting

CdS sensitized NiO electrode was used as the photoactive cathode in a photoelectrochemical cell for water splitting,avoiding the use of a sacrificial electron donor.Photocurrent increment under visible light irradiation was observed after integration of[Co（dmgH）₂（4-Me-py）Cl]（1） to the photocathode,suggesting 1 could accept electrons from photoexcited CdS for water reduction and NiO could move the holes in the valence band of CdS to anode for water oxidation.     

Trends in the detection of pharmaceuticals and endocrine-disrupting compounds by Field-Effect Transistors (FETs)

Field-effect transistors (FETs) are one of the most widely-used electronic sensors for continuous monitoring and detection of contaminants such as pharmaceuticals and endocrine-disrupting compounds at low concentrations. FETs have been successfully utilized for the rapid analysis of these environmental pollutants due to their advantageous material properties like the disposability, rapid responses and simplicity. This paper presented an up-to-date overview of applied strategies with different bio-based materials in order to enhance the analytical performances of the designed sensors. Comparison and discussion were made between characteristics of recently engineered FET bio-sensors used for the detection of famous and selected pharmaceutical compounds in the literature. The recent progress in environmental research applications, comments on interesting trends, current challenge for future research in endocrine-disrupting chemicals’ (EDCs) detection using FETs biosensors were highlighted.