Demonstration of Center Pivot Uniformity Evaluation and Retrofit to Improve Water Use Efficiency

Agricultural irrigation is a primary user for freshwater withdrawal. Irrigation plays an important role in crop production, as it provides the benefit of reducing the effects of prolonged dryness and erratic precipitation. Center pivot irrigation system is the most common irrigation system in agriculture. As the center pivot irrigation system ages, the system could develop a leaking joint, clogged sprinklers, and physical damage. This can cause areas of non-uniformity that can lead to under- or over-irrigated in some areas of the land, resulting in excess energy use and cost, wasting resources, and environmental impacts. Thus, it is important to evaluate the performance of a center pivot irrigation system regularly to maximize return on investments and minimize wasting resources. This study focuses on evaluating the impacts and benefits of improved center pivot irrigation distribution uniformity by performing distribution uniformity evaluations pre- and post-retrofit. This study also focused on demonstrating an unmanned aerial vehicle (UAV) to assess the performance of the center pivot irrigation system in two irrigated farmlands. The Coefficient of Uniformity (CU), Distribution Uniformity (DU), and Scheduling Coefficient (SC) were calculated based on the catch can test data. The values were utilized to evaluate water and energy savings from the improved coefficients. The team has found that replacing sprinkler packages increased the CU from 78 to 89 and the DU from 77 to 82, and reduced the SC from 1.3 to 1.2 in Field A. In Field B, replacing sprinkler packages increased the CU from 73 to 91 and the DU from 62 to 84 and reduced the SC from 1.6 to 1.2. The estimated water savings in Field A due to the reduced scheduling coefficient was approximately 151,000 liters/hectare/year, with consideration of the corn and soybean rotation field in Michigan. The estimated water savings in Field B was 608,000 liters/hectare/year. The data from this demonstration study showed the value of distribution uniformity evaluation and retrofit of irrigation systems. This information will encourage farmers and agricultural industries to consider performing more distribution uniformity evaluations, ultimately improving irrigation water use efficiency and supporting sustainable water management in agriculture.

Comparison of Methods for Estimating Crop Water Use: Sap Flow, FAO-56 Penman-Monteith, and Weather Parameters

Knowing crop water uptake each day is useful for developing irrigation scheduling. Many technologies have been used to estimate daily crop water use. Sap flow is one of the technologies that measure water flow through the stem of a plant and estimate daily crop water uptake. Sap flow sensor is an effective direct method for measuring crop water use, but it is relatively expensive and requires frequent maintenance. Therefore, alternative methods, such as evapotranspiration based on FAO 56 Penman-Monteith equation and other weather parameters were evaluated to find the correlation with sap flow. In this study, Dynamax Flow 32-1K sap flow system was utilized to monitor potato water use. The results show sap flow has a strong correlation with evapotranspiration (RMSE = 1.34, IA = 0.89, MBE = -0.83), solar radiation (RMSE = 2.25, IA = 0.72, MBE = -1.80), but not with air temperature, relative humidity, wind speed, and vapor pressure. It is worth noting that the R2 between sap flow and relative humidity was 0.55. This study has concluded that daily evapotranspiration and solar radiation can be used as alternative methods to estimate sap flow.

Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns

Non-ionic deep eutectic solvents(DESs)are non-ionic designer solvents with various applications in catalysis,extraction,carbon capture,and pharmaceuticals.However,discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation.The search for DES relies heavily on intuition or trial-and-error processes,leading to low success rates or missed opportunities.Recognizing that hydrogen bonds(HBs)play a central role in DES formation,we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning(ML)models to discover new DES systems.We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics(MD)simulation trajectories.The analysis reveals that DES systems have two unique features compared to non-DES systems:The DESs have①more imbalance between the numbers of the two intra-component HBs and②more and stronger inter-component HBs.Based on these results,we develop 30 ML models using ten algorithms and three types of HB-based descriptors.The model performance is first benchmarked using the average and minimal receiver operating characteristic(ROC)-area under the curve(AUC)values.We also analyze the importance of individual features in the models,and the results are consistent with the simulation-based statistical analysis.Finally,we validate the models using the experimental data of 34 systems.The extra trees forest model outperforms the other models in the validation,with an ROC-AUC of 0.88.Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

Effects of protein and lignin on cellulose and xylan anaylses of lignocellulosic biomass

Interactions of lignocellulosic components during fiber analysis were investigated using the highly adopted compositional analysis procedure from the National Renewable Energy Laboratory（NREL）,USA.Synthetic feedstock samples were used to study the effects of lignin/protein,cellulose/protein,and xylan/protein interaction on carbohydrate analysis.Disregarding structural influence in the synthetic samples,lignin and protein components were the most significant（P〈0.05）factors on cellulose analysis.Measured xylan was consistent and unaffected by content variation throughout the synthetic analysis.Validation of the observed relationships from synthetic feedstocks was fulfilled using real lignocellulosic feedstocks：corn stover,poplar,and alfalfa,in which similar results have been obtained,excluding cellulose analysis of poplar under higher protein content and xylan analysis of alfalfa under higher protein content.The results elucidated that according to their protein and lignin contents of different lignocellulosic materials,accuracy of the NREL method on cellulose and xylan analyses could be improved by applying a stronger extraction step to replace water/ethanol extraction.

Straw Segmentation Algorithm Based on Modified UNet in Complex Farmland Environment

Intelligent straw coverage detection plays an important role in agricultural production and the ecological environment.Traditional pattern recognition has some problems,such as low precision and a long processing time,when segmenting complex farmland,which cannot meet the conditions of embedded equipment deployment.Based on these problems,we proposed a novel deep learning model with high accuracy,small model size and fast running speed named Residual Unet with Attention mechanism using depthwise convolution(RADw–UNet).This algorithm is based on the UNet symmetric codec model.All the feature extraction modules of the network adopt the residual structure,and the whole network only adopts 8 times the downsampling rate to reduce the redundant parameters.To better extract the semantic information of the spatial and channel dimensions,the depthwise convolutional residual block is designed to be used in feature maps with larger depths to reduce the number of parameters while improving the model accuracy.Meanwhile,the multi–level attention mechanism is introduced in the skip connection to effectively integrate the information of the low–level and high–level feature maps.The experimental results showed that the segmentation performance of RADw–UNet outperformed traditional methods and the UNet algorithm.The algorithm achieved an mIoU of 94.9%,the number of trainable parameters was only approximately 0.26 M,and the running time for a single picture was less than 0.03 s.

Culture Age on Evaluation of Electrically Active Magnetic Nanoparticles as Accurate and Efficient Microbial Extraction Tools

A potential confounding factor in the development and evaluation of biosensors is the diverse nature of the disciplines involved. Biosensor technology involves electrochemistry, microbiology, chemical synthesis, and engineering, among many other disciplines. Biological systems, due to non-homogeneous distribution, are already imprecise compared with other systems, especially food based systems. Inadequate knowledge of the techniques to moderate this leads to ineffective evaluation strategies and potentially halting the pursuit of excellent technology that was merely poorly evaluated. This research was undertaken to evaluate the effect culture age had on the capture efficiency of the electrically active magnetic nanoparticles (EAMNP) using culture as the evaluation tool. The age of culture used for immunomagnetic separation (IMS) over all the experiments was 6 to 18 hours. Ideal culture age range for evaluating biosensors is 4 to 10 hours according to the growth curve for E. coli O157: H7 in trypticase soy broth. This is supported by the statistically significant difference among organisms in groups from 3 to 10 hours old compared with those grouped from 11 to 18 and >19 hours old (α = 0.05, p = 0.001 and p = 0.014 respectively). The two older categories were not different from each other. The capture efficiency in all biosensor analysis will vary less than when culture of only viable cells is the diagnostic tool. This allows a true evaluation of the consistency and accuracy of the method, less hindered by the variation in the ability to culture the organism.

Switchgrass (<i>Panicum virgatum</i>) Fermentation by <i>Clostridium thermocellum</i>and <i>Clostridium beijerinckii</i>Sequential Culture: Effect of Feedstock Particle Size on Gas Production

Fermentation of cellulosic biomass can be done in a single step with cellulolytic, solventogenic bacteria, such as Clostridium thermocellum. However, the suite of products is limited in consolidated bioprocessing. Fortunately, the thermophilic nature of C. thermocellum can be exploited in sequential culture. Experiments were conducted to determine the effect of feedstock particle size on fermentation by sequential cultures and to demonstrate this effect could be shown by gas production. Dual-temperature sequential cultures were conducted by first culturing with C. thermocellum (63°C, 48 h) before culturing with C. beijerinckii (35°C, 24 h). Switchgrass (2, 5 or 15 mm particle size) was the feedstock in submerged substrate (10% w/v) fermentation. The extent of fermentation was evaluated by gas production and compared by analysis of variance with Tukey’s test post hoc. C. thermocellum alone produced 78 kPa cumulative pressure (approx. 680 mL gas) when the particle size was 2 or 5 mm. The C. thermocellum cultures with 15 mm feedstock particles had a mean cumulative pressure of 15 kPa after 48 h, which was less than the 2 and 5 mm treatments (P °C) and inoculated with C. beijerinckii, and the cumulative pressures were reset to ambient, cumulative pressure values as great as 70 kPa (equivalent to an additional 670 mL gas) were produced in 24 h. Again, the longer (15 mm) particle size produced less gas (P < 0.05). When the substrates were inoculated with C. beijerinckii without previous fermentation by C. thermocellum, the mean cumulative pressures were approximately 10 kPa. These results indicate that biological pretreatment with C. thermocellum increased the availability of switchgrass carbohydrates to C. beijerinckii, and that gas production is suitable method to show the effectiveness of a pretreatment.

Phylogenetic Analysis of Anaerobic Co-Digestion of Animal Manure and Corn Stover Reveals Linkages between Bacterial Communities and Digestion Performance

Over 3 million tons of manures are produced annually in the United States and pose environmental and health risks if not remediated. Anaerobic digestion is an effective method in treating organic wastes to reduce environmental impacts and produce methane as an alternative energy. Previous studies suggested that optimization of feed composition, hydraulic retention time, and other operational conditions can greatly improve total solids removal and increase methane productivity. These environmental factors improve functionality by altering the microbial community structure but explicit details of how the bacterial community shifts are poorly understood. Our investigations were conducted to investigate the relationship between environmental factors, microbial community structure and bioreactor efficiency by using metagenomic analysis of the microbial communities. Our results indicated that the bioreactor with the greatest methane production, digestion efficiency and reduced levels of E. coli/Shigella had a distinctive community structure at the genus level with unique and abundant uncultivated strains of Bacteroidetes. Moreover the same bioreactor was enriched in Aminomonas paucivorans and Clostridia populations that can utilize secondary metabolites produced during cellulose/hemicellulose degradation to generate hydrogen and acetate. Hence specific digestion conditions that enrich for these populations may provide a route to the optimization of co-digestion systems and control the variability in reactor performance.

高光谱图像技术在水果品质无损检测中的应用

传统的近红外光谱分析法和可见光图像技术应用于水果品质无损检测中存在的检测区域小、检测时间长、仅能检测表面情况等局限性。高光谱图像技术结合光谱技术与计算机图像技术两者的优点,可获得大量包含连续波长光谱信息的图像块,其图像信息可检测水果的外部品质,光谱信息则可用于水果内部品质的检测,达到根据水果内、外部综合品质进行分类的目的。根据不同的采集设备,简介了两种获得高光谱图像的方法。综述了国内外将该技术应用于水果品质检测方面的研究进展,检测内容包括外观品质、损伤与缺陷,成熟度和坚实度,含糖量、含水率等内部品质,着重介绍了各高光谱图像的成像波段范围、分辨率、成像源,实验数据处理的方法以及实验结果等。根据综述所得提出了高光谱图像技术应用中需要解决的光谱降维、降低样品差异影响和实时检测平台搭建等问题。

果园风送喷雾精准控制方法研究进展

果园风送喷雾技术与装备正在朝着精准化和智能化方向发展。果园喷雾控制对象主要为喷施药量和风力供给量,二者需要协同精准调控,其按需调控的前提是果园靶标精准探测。该文从果园靶标探测方法、喷施药量控制方法、风力调控方法 3个方面对现有研究进展进行综述,阐述了基于光电感知、超声波传感、激光雷达、图像、光谱和电子鼻技术探测果树位置、冠层外形轮廓、冠层体积、冠层内部结构、枝叶稠密程度、病虫害程度等特征信息的技术方法;分析了喷施药量调控方法中管道总药量控制方法在管道设计、混药方式、药液流量控制策略方面技术和产品化上取得的巨大突破,以及喷头药量独立控制方法研究方面获得的大量成果;综述了果园风送喷雾风速风量需求理论原则、风场雾场建模方法、风力调控方法与调控装备研究进展,指出了其基本理论原则、建模调控方法等科学问题还有待深入探索。同时,还分析了目前研究在果园靶标探测方法、喷施药量调控方法和风送喷雾风力调控方法中面临的困难和挑战,主要包括冠层稠密程度和病虫害程度高效感知方法探索、靶标风力需求普适模型构建、风场建模风力按需调控方法研究和精准喷雾技术与系统集成开发。最后指出了果园风送喷雾精准控制方法未来发展方向:1)果园靶标冠层枝叶稠密程度和病虫害程度在线探测方法将成为新的研究热点;2)果园风送喷雾风速风量供给需求理论原则、风场快速模拟仿真和风力调控方法与装备是未来重要研究方向;3)随着高新科技的涌现,科研院所和公司有望在果园喷雾药量和风力调控系统优化设计及精准喷雾机系统集成研发方面获得更大发展。

农田土壤含水率监测的无线传感器网络系统设计

为解决传统土壤含水率监测中所存在的监测区域面积小、采样率低等问题,设计和开发了基于无线传感器网络技术的土壤含水率监测系统,包括10个传感器节点,1个簇首和1个基站节点,可按任意时间间隔全自动地采集、处理、传输和存储地表以下4个不同土层土壤含水率变化状况;各类节点采用TinyOS操作系统,节点间通信遵循ZigBee协议;含水率测量采用EC-5传感器;太阳能供电模块的供电能力满足传感器节点及簇首的能耗需求;进行了数据包传输率试验,10个传感器节点中有7个的数据包正确传输率高于90%,1个节点的数据包正确传输率为89.2%,2个节点的传输率低于70%。造成2个节点数据包传输率较低的主要原因是太阳能供电电路制作,通过更换电路板解决了该问题。试验结果表明,系统能够实现稳定的数据传输,适合农田土壤含水率的实时监测。

基于神经网络预测的无线传感器网络田间射频信号路径损耗

为解决应用无线传感器网络技术监测农田信息时无法快速预测射频信号路径损耗的问题,基于神经网络理论研究了田间路径损耗与其影响因素间的关系。试验中选取915和2470MHz2个载波频率,在冬小麦的不同生长阶段测量射频信号在田间各影响因素作用下的路径损耗,建立和验证基于神经网络的射频信号田间路径损耗预测模型。所建立模型模拟值与实测值的相关系数为0.92,应用建立的神经网络预测田间射频信号路径损耗并与实测值对比,最大预测误差绝对值为4.186dB,最大预测标准差为2.759dB,预测准确度为94.2%。所建立的BP网络可以对田间射频信号路径损耗进行预测。

果园风送喷雾机的改进设计与喷雾高度调控模型构建

【目的】针对第一代果园喷雾机存在的风箱角度不能电动控制且调节精度不高、传动装置设计欠合理和体积偏大等问题进行改进,并建立喷雾高度调控模型,为新一代果园喷雾机的设计提供参考。【方法】对喷雾机导流风箱的固定方式、传动方案和通过性能进行了改进,并通过喷雾沉积分布试验及数学方程拟合,建立沉积分布重心高度、喷雾宽度与风箱角度之间的数学模型。【结果】与改进前相比,该喷雾机不仅支持风箱角度电动精确调控,而且其理论功耗还减少了8%,长度尺寸减小了32.6%。风箱旋转角度对喷雾机尺寸影响的测试结果表明,旋转角度对风箱组宽度有一定的影响,旋转角度为最大值30°时风箱组宽度最大,为0.95m,但仍然小于喷雾机轮距1.0m;风箱旋转角度对喷雾机高度基本没有影响。改进后的喷雾机喷雾沉积分布试验表明,风箱与铅垂线夹角分别为0°,15°和30°时,喷雾沉积分布重心高度分别为1.14,1.55和2.20m,70.7%的药量分别垂直集中分布于0.72~1.56,1.15~1.94和1.71~2.69m,拟合得到的沉积分布重心高度、喷雾宽度和风箱角度之间的数学方程均为线性方程,其R2值分别为0.982和0.996。【结论】改进后喷雾机支持喷雾高度电动精确调节;所建立的喷雾高度智能调控模型可为果园喷雾机的改进完善提供支持。

食品中核磁共振状态图的应用(英文)

食品中质量感官指标和货架期与它的理化特性有关,水是食品原料中重要组成部分,它的含量与食品的理化特性有关,并在其中扮演着重要角色。“玻璃态转化” 的概念是在研究食品原料和产品理化特性的聚合物理论时提出的。食品玻璃态转化状态图通常用来描述水含量与食品理化特性的关系。状态图能有效地反映在不同温度和水分含量下的物质的理化特性。本文提出一个新的观念—“核磁共振状态图” 概念,核磁共振状态图是指核磁共振弛豫时间(通常指自旋-自旋弛豫时间或T2)和温度之间的曲线图,它放映食品的核磁共振弛豫现象与食品的理化特性关系。我们在研究时发现这条曲线图与物质的许多理化特性有着良好的相关性。利用核磁共振状态图可以在以下方面得到应用:(1)通过分析产品的成分,确定产品变化的规律;(2)预测产品的理化性质的变化(质地、粘性、结块、水分和油脂的迁移等)的趋势,营养成分降解和微生物代谢与食品中水分活度之间的关系;(3)结合核磁共振成像技术能很好地帮助理解水分(特别游离水分)对食品的质量与安全的影响。本文描述了核磁共振的基本原理、核磁共振弛豫现象的特征与食品特性的关系,并为研究食品体系提供新的研究方法和途径。

基于空间可分辨光谱的番茄成熟度判别方法研究

对基于空间可分辨光谱的番茄成熟度分类判别方法进行了试验研究。首先根据番茄的内部颜色,将600个番茄分为6个不同成熟度(green,breaker,turning,pink,light red and red),然后用自行开发的多通道高光谱成像探头采集番茄的空间可分辨(SR)光谱,建立基于空间可分辨光谱的番茄成熟度偏最小二乘判别(PLSDA)模型和支持向量机判别(SVMDA)模型。结果显示,对于PLSDA模型,SR光谱15为最佳分类光谱,分类正确率达到81.3%;对于SVMDA模型,SR光谱10为最佳预测分类光谱,分类正确率为86.3%。对六个成熟度等级番茄的判别分类,SVMDA模型要明显优于PLSDA模型。此外,相对于较小的光源-检测器距离SR光谱,较大的光源-检测器距离SR光谱可以获得更好的判别效果,显示出空间可分辨光谱在果蔬品质检测方面的应用潜力。

影响鱼类分布的生态水文因子建模及分析(英文)

人类活动干扰下河流自然水势改变,导致鱼类栖息地环境变化。为准确分析影响鱼类分布的生态水文环境,该文以美国阿肯色河流域的国家级濒危小型鱼类:产漂流性卵鲤鱼(Arkansas River Shiner,Notropis girardi,ARS)为例,根据其繁殖期特有的水文条件需求,选取流域40个水文站点的日径流数据,分别计算历史时期(1950-1962年)和当前时期(1990-2010年)的3个繁殖期生态水文因子:高流量,较高流量历时及断流历时。每个生态水文因子分别结合其他25个环境因子,基于最大熵理论(Max Ent)构建物种分布模型。运用经典受试者工作曲线(receiver operating characteristic,ROC)及Parolo等提出的模型转移前后预测结果的Spearman’s rho相关系数,评价模型的自适应性(model fit)和转移能力(transferability)。3个模型在两个时期的ROC曲线与横坐标围成的面积均大于0.95,模型转移前后预测结果的Spearman’s rho相关系数均大于0.6。研究分析表明,模型具有较好的自适应性及转移性。3个生态水文因子中,高流量对模型的贡献率最大,对该种鱼类在两个时期的潜在分布影响最显著。不同的历史时期,物种的潜在适生概率随同一生态水文因子的变化呈现出了不一致的变化趋势。3个模型均预测出ARS的潜在生境有相同的空间变化趋势。该研究结果能够为中国保护河流生态系统与濒危鱼类提供借鉴经验。

基于流量偏差率的滴灌毛管管径简易设计（英文）

满足灌水均匀度设计标准与额定流量的滴灌毛管管径优化设计是滴灌毛管水力设计的重要内容。该文以流量偏差率为灌水均匀度设计标准,提出一种简易的滴灌毛管管径水力设计方法。通过改变单向和双向滴灌毛管流量偏差率解析公式的形式,提出一个新的管径设计参数。基于能坡线法,建立参数与坡降比参数的关系,并绘制和构建相关图形和计算公式。结果表明：当流量指数分别取值为1.75、1.69和1.00时,单向毛管管径的设计参数取值分别为-1～≤2.801,-1～≤2.859和-1～≤4;双向毛管管径的设计参数取值分别为0～≤3.143,0～≤3.183和0～≤4。满足流量偏差率设计标准和灌水器额定流量时,单向毛管管径将有1或2个设计值,双向毛管管径将有1、2或多个设计值。根据参数的取值范围,以坡降比参数为设计变量,推导了均匀坡下单向和双向毛管管径的计算公式。此外,进一步简化了滴灌毛管进口工作水头的计算公式。当已知设计流量,流量偏差率设计标准,毛管管长等参数时,可以按照设计步骤直接计算滴灌毛管管径和进口工作水头,无需进行任何复杂的试算。设计案例1表明在多数地形条件下,利用该文方法设计单向毛管的结果与传统方法设计结果的相对误差不超过4%;设计案例2表明该文方法设计双向毛管的结果与其他2种常用方法设计结果的最大相对误差为4%。因此,从工程实践角度,利用该方法设计的滴灌毛管管径和进口工作水头与其他传统方法设计结果非常接近。相比于传统设计方法,该方法简便实用,可以直接应用于滴灌工程设计。该研究可为改进滴灌毛管优化设计提供理论依据。

美国赠地大学工科研究生教育的特色分析

美国研究生教育在世界教育行业中占有重要地位,它从19世纪开始至今已经形成了一套完善的培养制度。论文从其录取政策、培养目标、培养机制、考核办法和质量评估体系等方面进行了较为详细的分析,并针对我国研究生教育现状提出了一些启示。

Newly-simplified method for hydrauli design of micro-irrigation laterals based on emission uniformity

An analytical approach was developed to design a single uniformly sloping lateral in the micro-irrigation systems.Emission uniformity was used as the water application uniformity criterion.Energy relations based on the energy-gradient-line approach were revamped to account for the spatial variance of emitter outflow and the emitter connections local energy losses.Four pressure head grade line profiles were distinguished:uphill,horizontal,gentle downhill and steep downhill.Analytical expressions of emission uniformity by hydraulic variation for each pressure profile were developed based on the design variables:length and diameter of lateral,emitter spacing,emitter flow equation parameters,equivalent length characterizing local losses and ground slope.The design conditions for selecting emitter type,the number of emitters per plant and designing the diameter of the uphill and steep downhill laterals were also developed.The nonlinear equations for determining lateral diameter and lateral length were solved iteratively by using the built-in root-finding function of(Tools>Goal Seek…)in the calculation spreadsheet of Microsoft Excel.The procedures also provide the options to fix the design lateral diameter with the commercial standard size or fix the design lateral length based on the field size.The operating inlet pressure and maximum amplitude of the pressure head throughout the lateral could also be determined easily by the procedure.Two numerical applications with various slope combinations indicate that the proposed analytical approach produces results close to the accurate stepwise numerical solutions.In comparison with Keller method,the proposed approach could produce more appropriate designs.

Synthesis of ternary magnetic nanoparticles for enhanced catalytic conversion of biomass-derived methyl levulinate into γ-valerolactone

Conversion of levulinic acid and its esters into versatile y-valerolactone(GVL)is a pivotal and challenging step in biorefineries,limited by high catalyst cost,the use of hydrogen atmosphere,or tedious catalyst preparation and recycling process.Here we have successfully synthesized a ternary magnetic nanoparticle catalyst(Al_(2)O_(3)-ZrO_(2)/Fe_(3)O_(4)(5)),over which biomass-derived methyl levulinate(ML)can be quantitively converted to GVL with an extremely high selectivity of>99%and yield of-98%in the absence of molecular hydrogen.Al_(2)O_(3)-ZrO_(2)/Fe_(3)O_(4)(5)incorporates simultaneously inexpensive alumina and zirconia onto magnetite support by a facile coprecipitation method,giving rise to a core-shell structure,welldistributed acid-base sites,and strong magnetism,as evidenced by the X-ray diffraction(XRD),X-ray photoelectron spectroscopy(XPS),scanning electron microscopy(SEM),transmission electron microscopy(TEM),high-angle annular dark-field scanning-TEM(HAADF-STEM),SEM-energy dispersive Xray spectroscopy(SEM-EDX),temperature-programmed desorption of ammonia(NH3-TPD),temperature-programmed desorption of carbon dioxide(CO_(2)-TPD),pyridine-adsorption infrared spectra(Py-IR),and vibrating sample magnetometry(VSM).Such characteristics enable it to be highly active and easily recycled by a magnet for at least five cycles with a slight loss of its catalytic activity,avoiding a time-consuming and energy-intensive reactivation process.It is found that there was a synergistic effect among the metal oxides,and the high efficiency and selectivity originating from such synergism are evidenced by kinetic studies.Furthermore,a reaction mechanism regarding the hydrogenation of ML to GVL is proposed by these findings,coupled with gas chromatography-mass spectrometry(GC-MS)analysis.Accordingly,this readily synthesized and recovered magnetic nanocatalyst for conversion of biomassderived ML into GVL can provide an eco-friendly and safe way for biomass valorization.