Effect of slow-release iron fertilizer on iron-deficiency chlorosis, yield and quality of Lilium davidii var.unicolor in a two-year field experiment

Iron deficiency chlorosis of Lilium davidii var. unicolor is often the case in practice in alkaline soils of northwest region of China. It is difficult to control iron chlorosis because of high cost and short effective work time of conventional iron fertilizers. In this study, a 2-year field experiment was conducted to evaluate the effects of two slow-release fertilizers on the suppression of iron deficiency chlorosis, soil chemical properties, and the yield and quality of L. davidii var. unicolor. Results show that both coated slow-release iron fertilizers and embedded slow-release iron fertilizer effectively controlled iron-deficiency chlorosis. The application of slow-release iron fertilizers significantly increased plant height and chlorophyll content of L. davidii var. unicolor at different growth stages. Furthermore, coated iron fertilizer application significantly increased starch, protein, soluble sugar and vitamin C content of L. davidii var. unicolor, and it also significantly improved total amino acid content, with increases in essential amino acids(Trp, Leu, Lys, Phe, Val, and Thr contents) and in nonessential amino acids(Asp, Glu, Cit, Ihs, Acc, Ala, Pro, and Cys contents). It was concluded that application of coated slow-release iron fertilizer could be a promising option for suppression of iron deficiency chlorosis and deserves further study.

An artificial synapse by superlattice-like phase-change material for low-power brain-inspired computing

Phase-change material(PCM)is generating widespread interest as a new candidate for artificial synapses in bioinspired computer systems.However,the amorphization process of PCM devices tends to be abrupt,unlike continuous synaptic depression.The relatively large power consumption and poor analog behavior of PCM devices greatly limit their applications.Here,we fabricate a GeTe/Sb2Te3 superlattice-like PCM device which allows a progressive RESET process.Our devices feature low-power consumption operation and potential high-density integration,which can effectively simulate biological synaptic characteristics.The programming energy can be further reduced by properly selecting the resistance range and operating method.The fabricated devices are implemented in both artificial neural networks(ANN)and convolutional neural network(CNN)simulations,demonstrating high accuracy in brain-like pattern recognition.

Effects of gibberellic acid on tiller-bulb number and growth performance of Lilium davidii var. unicolor

Lilium davidii var.unicolor(Lanzhou lily)is an important economic crop in the northwest cold and arid regions of China.Effective regulation of tiller-bulb development and plant growth is the key to improving yield and quality of the lily.This study attempted to evaluate the effect of gibberellic acid(GA3)on tiller-bulb development and plant growth of Lanzhou lily by applying GA3 at various concentrations(0 mg/L,10 mg/L,30 mg/L,60 mg/L,and 100 mg/L)before planting and in the seedling period.Results showed that the 60-mg/L GA3 application had an inhibiting effect on tiller-bulb formation and increased the ratios of single and double bulbs but decreased the ratios of bulbs with three or more tiller bulbs,as com pared to the control(CK)and other GA3 treatments.The difference in flower number did not reach significant levels among the treatments.The tillering-related endogenous hormones IAA(indole-3-acetic acid)and Z(zeatin)content de creased,while IAA/Z increased with the 60-mg/L GA3 treatment during tiller initiation.And also,the shoot-bulb number and total daughter-bulb number decreased significantly with the 60-mg/L GA3 treatment.Furthermore,the 10-mg/L GA3 application promoted growth of Lanzhou lily significantly and resulted in an increase in plant height;bulb diameter;bulb circumference;and biomass of shoots,bulbs,fibrous roots,and the whole plant.Therefore,GA3 application is promising as a new regulation method for inhibiting tiller-bulb development and promoting bulb growth in Lanzhou lily production.

Modification strategies for non-aqueous, highly proton-conductive benzimidazole-based high-temperature proton exchange membranes

High-temperature proton exchange membranes(HT-PEMs) possess excellent thermal and outstanding electrochemical stability, providing an avenue to realize high-temperature proton exchange membranes fuel cells(HT-PEMFCs) with both superior power density and long-term durability. Unfortunately, polybenzimidazole(PBI), a typical material for conventional HT-PEMs, fails to compromise the high nonaqueous proton conductivity and high mechanical properties, thus hindering their practical applications.Achieving efficient nonaqueous proton conduction is crucial for HT-PEMFC, and many insightful research works have been done in this area. However, there still lacks a report that integrates the host-guest interactions of phosphoric acid doping and the structural stability of polymers to systematically illustrate modification strategies. Here, we summarize recent advancements in enhancing the nonaqueous proton conduction of HT-PEMs. Various polymer structure modification strategies, including main chain and side group modification, cross-linking, blocking, and branching, are reviewed. Composite approaches of polymer, including compounding with organic porous polymers, filling the inorganic components and modifying with ionic liquids, etc., are also covered in this work. These strategies endow the HT-PEMs with more free volume, nanophase-separated structure, and multi-stage proton transfer channels, which can facilitate the proton transportation and improve their performance. Finally, current challenges and future directions for further enhancements are also outlined.

Simulation of inhomogeneous strain in Ge-Si core-shell nanowires

This paper studies the elastic deformation field in lattice-mismatched Ge-Si core-shell nanowires(NWs).Infinite wires with a cylindrical cross section under the assumption of translational symmetry are considered.The strain distributions are found by minimizing the elastic energy per unit cell using finite element method.This paper finds that the trace of the strain is discontinuous with a simple,almost piecewise variation between core and shell,whereas the individual components of the strain can exhibit complex variations.The simulation results are prerequisite of strained band structure calculation,and pave a way for further investigation of strain effect on the related transport property simulation.

基于Ge-Ga-Sb介质的全相变脉冲神经网络的设计

人工脉冲神经网络通常由多个异质结构的电路单元构成,其中包括具备积分点火功能来产生脉冲信号的神经元模拟器,以及具备记忆功能的突触器件.在本文中,我们设计了一种能进行“同质集成”的相变存储介质Ge-Ga-Sb(GGS)器件,该器件能够同时实现神经元和突触的模拟.在先前的研究中,GGS材料表现出优秀的数据存储功能,例如它具备较高的工作温度(281℃)、较高的十年数据保存温度(230℃)以及较低的电阻漂移.当对该器件改用短脉冲电学操作时,GGS器件首先会发生几个数量级的电阻突变,然后紧接着发生连续的电阻降低.通过透射电子显微镜发现,电阻突变是因为电极之间产生了结晶的导电通道,而电阻缓变是因为导电通道的变粗以及在通道内产生材料分相所致.这种“突变-缓变”的电阻变化特性既可以用来模拟神经元的积分点火功能,也可以模拟突触权重的变化.基于此器件设计的全相变脉冲神经网络,可以实现高达90%的手写数字识别率.

Self-selective memristor-enabled in-memory search for highly efficient data mining

Similarity search,that is,finding similar items in massive data,is a fundamental computing problem in many fields such as data mining and information retrieval.However,for large-scale and high-dimension data,it suffers from high computational complexity,requiring tremendous computation resources.Here,based on the low-power self-selective memristors,for the first time,we propose an in-memory search(IMS)system with two innovative designs.First,by exploiting the natural distribution law of the devices resistance,a hardware locality sensitive hashing encoder has been designed to transform the realvalued vectors into more efficient binary codes.Second,a compact memristive ternary content addressable memory is developed to calculate the Hamming distances between the binary codes in parallel.Our IMS system demonstrated a 168energy efficiency improvement over all-transistors counterparts in clustering and classification tasks,while achieving a software-comparable accuracy,thus providing a low-complexity and low-power solution for in-memory data mining applications.

Complementary memtransistors for neuromorphic computing: How, what and why

Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic computing.On the other side,it is known that the complementary metal-oxide-semiconductor(CMOS)field effect transistors have played the fundamental role in the modern integrated circuit technology.Therefore,will complementary memtransistors(CMT)also play such a role in the future neuromorphic circuits and chips?In this review,various types of materials and physical mechanisms for constructing CMT(how)are inspected with their merits and need-to-address challenges discussed.Then the unique properties(what)and poten-tial applications of CMT in different learning algorithms/scenarios of spiking neural networks(why)are reviewed,including super-vised rule,reinforcement one,dynamic vision with in-sensor computing,etc.Through exploiting the complementary structure-related novel functions,significant reduction of hardware consuming,enhancement of energy/efficiency ratio and other advan-tages have been gained,illustrating the alluring prospect of design technology co-optimization(DTCO)of CMT towards neuro-morphic computing.

Toward memristive in-memory computing:principles and applications

With the rapid growth of computer science and big data,the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and memories.Memristive in-memory computing paradigm is considered as a prominent candidate to address these issues,and plentiful applications have been demonstrated and verified.These applications can be broadly categorized into two major types:soft computing that can tolerant uncertain and imprecise results,and hard computing that emphasizes explicit and precise numerical results for each task,leading to different requirements on the computational accuracies and the corresponding hardware solutions.In this review,we conduct a thorough survey of the recent advances of memristive in-memory computing applications,both on the soft computing type that focuses on artificial neural networks and other machine learning algorithms,and the hard computing type that includes scientific computing and digital image processing.At the end of the review,we discuss the remaining challenges and future opportunities of memristive in-memory computing in the incoming Artificial Intelligence of Things era.