Self-assembly of perovskite nanocrystals:From driving forces to applications

Self-assembly of metal halide perovskite nanocrystals(NCs)into superlattices can exhibit unique collective properties,which have significant application values in the display,detector,and solar cell field.This review discusses the driving forces behind the self-assembly process of perovskite NCs,and the commonly used self-assembly methods and different self-assembly structures are detailed.Subsequently,we summarize the collective optoelectronic properties and application areas of perovskite superlattice structures.Finally,we conclude with an outlook on the potential issues and future challenges in developing perovskite NCs.

Alcohol solvent effect on the self-assembly behaviors of lignin oligomers

The interactions between lignin oligomers and solvents determine the behaviors of lignin oligomers self-assembling into uniform lignin nanoparticles(LNPs).Herein,several alcohol solvents,which readily interact with the lignin oligomers,were adopted to study their effects during solvent shifting process for LNPs’production.The lignin oligomers with widely distributed molecular weight and abundant guaiacyl units were extracted from wood waste(mainly consists of pine wood),exerting outstanding self-assembly capability.Uniform and spherical LNPs were generated in H_(2)O-n-propanol cosolvent,whereas irregular LNPs were obtained in H_(2)O-methanol cosolvent.The unsatisfactory self-assembly performance of the lignin oligomers in H_(2)O-methanol cosolvent could be attributed to two aspects.On one hand,for the initial dissolution state,the distinguishing Hansen solubility parameter and polarity between methanol solvent and lignin oligomers resulted in the poor dispersion of the lignin oligomers.On the other hand,strong hydrogen bonds between methanol solvent and lignin oligomers during solvent shifting process,hindered the interactions among the lignin oligomers for self-assembly.

Chitosan/Sodium Alginate Multilayer pH-Sensitive Films Based on Layer-by-Layer Self-Assembly for Intelligent Packaging

The abuse of plastic food packaging has brought about severe white pollution issues around the world.Developing green and sustainable biomass packaging is an effective way to solve this problem.Hence,a chitosan/sodium alginate-based multilayer film is fabricated via a layer-by-layer(LBL)self-assembly method.With the help of superior interaction between the layers,the multilayer film possesses excellent mechanical properties(with a tensile strength of 50 MPa).Besides,the film displays outstanding water retention property(blocking moisture of 97.56%)and ultraviolet blocking property.Anthocyanin is introduced into the film to detect the food quality since it is one natural plant polyphenol that is sensitive to the pH changes ranging from 1 to 13 in food when spoilage occurs.It is noted that the film is also bacteriostatic which is desired for food packaging.This study describes a simple technique for the development of advanced multifunctional and fully biodegradable food packaging film and it is a sustainable alternative to plastic packaging.

Hollow tubes constructed by carbon nanotubes self-assembly for CO_(2) capture

Carbon nanotubes(CNTs)have garnered significant attention in the fields of science,engineering,and medicine due to their numerous advantages.The initial step towards harnessing the potential of CNTs involves their macroscopic assembly.The present study employed a gentle and direct self-assembly technique,wherein controlled growth of CNT sheaths occurred on the metal wire’s surface,followed by etching of the remaining metal to obtain the hollow tubes composed of CNTs.By controlling the growth time and temperature,it is possible to alter the thickness of the CNTs sheath.After immersing in a solution containing 1 g/L of CNTs at 60℃for 24 h,the resulting CNTs layer achieved a thickness of up to 60μm.These hollow CNTs tubes with varying inner diameters were prepared through surface reinforcement using polymers and sacrificing metal wires,thereby exhibiting exceptional attributes such as robustness,flexibility,air tightness,and high adsorption capacity that effectively capture CO_(2) from the gas mixture.

A Non-parametric Gradient-Based Shape Optimization Approach for Solving Inverse Problems in Directed Self-Assemblyof Block Copolymers

We consider the inverse problem of finding guiding pattern shapes that result in desired self-assembly morphologies of block copolymer melts.Specifically,we model polymer selfassembly using the self-consistent field theory and derive,in a non-parametric setting,the sensitivity of the dissimilarity between the desired and the actual morphologies to arbitrary perturbations in the guiding pattern shape.The sensitivity is then used for the optimization of the confining pattern shapes such that the dissimilarity between the desired and the actual morphologies is minimized.The efficiency and robustness of the proposed gradient-based algorithm are demonstrated in a number of examples related to templating vertical interconnect accesses(VIA).

Layer by Layer Self-assembly Fiber-based Flexible Electrochemical Transistor

Poly(3,4-ethylenedioxyethiophene)-polystyrene sulfonic acid(PEDOT:PSS)/polyallyl dimethyl ammonium chloride modified reduced graphene oxide(PDDA-rGO)was layer by layer self-assembled on the cotton fiber.The surface morphology and electric property was investigated.The results confirmed the dense membrane of PEDOT:PSS and the lamellar structure of PDDA-rGO on the fibers.It has excellent electrical conductivity and mechanical properties.The fiber based electrochemical transistor(FECTs)prepared by the composite conductive fiber has a maximum output current of 8.7 mA,a transconductance peak of 10 mS,an on time of 1.37 s,an off time of 1.6 s and excellent switching stability.Most importantly,the devices by layer by layer self-assembly technology opens a path for the true integration of organic electronics with traditional textile technologies and materials,laying the foundation for their later widespread application.

结合SAM大模型和数学形态学的历史地图水系信息提取方法

历史地图记载着丰富的历史地理信息,能够帮助了解历史规律,为当代发展提供借鉴。不同于现代地图、遥感影像等数据,历史地图保存时间久,存在留存数量少、图像精度低等问题,地图符号也与现代有所差异,因此信息难以被高效提取。针对该问题,本文以历史地图《宁夏省境黄河沿岸沟渠水道地形图》为试验数据,提出一种智能化历史地图水系信息提取方法。首先,结合符号句法,运用聚类与数学形态学方法构建数据集;然后,改进通用大模型(SAM)结构并进行迁移学习优化权重;最后,借助改进SAM自动提取历史地图水系信息。将试验结果与其他模型作对比,显示本文方法提取结果边界清晰,轮廓完整,准确率、精度等指标均为最高。同时,将提取结果与该区域水系现状作对比,发现历史上的河流沟渠如今大多改道、偏移或消失,湖泊面积大大减小。本文方法基于SAM通用大模型进行改进,验证了大模型在地图领域的可用性,为地图信息提取提供了思路。

Ⅰ型倍增层对异质SAM结构InSb-APD红外探测器性能的影响

红外探测器的光电特性会受到内部结构倍增层参数的影响,为了能够改善器件的雪崩效应,借助仿真软件Silvaco-TCAD,详细探讨了Ⅰ型倍增层的残余掺杂浓度和厚度对异质SAM结构InSb-APD红外探测器性能的影响。研究结果表明,随着Ⅰ型倍增层掺杂浓度的增加,其倍增层内的电场强度峰值增加,同时光响应度略微增加;随着Ⅰ型倍增层厚度的增加,其倍增层的光响应度与暗电流密度升高,同时电场强度峰值减少。进一步研究表明,当Ⅰ型倍增层残余掺杂浓度和厚度分别为1×10^(15)cm^(−3)和3μm时,有利于雪崩过程。

SAM在遥感影像通用语义分割中的应用

Segment Anything Model(SAM)是Meta公司最新发布的一个处理图像分割任务的通用模型,与FCN、U-Net等只能处理特定类型图片的图像分割模型不同,SAM具有较强的泛化能力。本文简述了目前主要的图像分割模型及特点,提出了基于SAM的遥感影像通用语义分割方法,研发了软件平台,并详细阐述了各功能模块的设计思路。实践表明,SAM在遥感影像通用分割方面具有良好的应用效果,研究成果在自然资源调查监测、农作物种植结构提取等工作中得到广泛应用。

视觉大模型SAM在医学图像分割中的应用综述

随着大模型技术的不断发展,以分割一切模型(segment anything model,SAM)为代表的视觉大模型在图像分割领域取得重要突破。SAM通过提示驱动完成一系列下游分割任务,旨在统一解决所有的图像分割问题。因此,将SAM应用于医学图像分割具有重要意义,其泛化性能够适应多种医学图像,为医生提供更全面的解剖结构和病变信息。介绍了图像分割常用的数据集;对SAM的网络结构和泛化性进行细致阐述;重点对SAM应用在全切片成像、磁共振成像、计算机断层扫描、超声和多模态图像的五大类医学图像进行梳理分析,总结优缺点和相应的改进方法;结合当前医学图像分割领域中存在的实际问题,讨论并展望了SAM未来的发展方向。

Combination therapy to overcome ferroptosis resistance by biomimetic self-assembly nano-prodrug

Ferroptosis has emerged as a potent form of no-apoptotic cell death that offers a promising alternative to avoid the chemoresistance of apoptotic pathways and serves as a vulnerability of cancer.Herein,we have constructed a biomimetic self-assembly nano-prodrug system that enables the co-delivery of gefitinib(Gefi),ferrocene(Fc)and dihydroartemisinin(DHA)for the combined therapy of both ferroptosis and apoptosis.In the tumor microenvironment,this nano-prodrug is able to disassemble and trigger drug release under high levels of GSH.Interestingly,the released DHA can downregulate GPX4 level for the enhancement of intracellular ferroptosis from Fc,further executing tumor cell death with concomitant chemotherapy by Gefi.More importantly,this nano-prodrug provides highly homologous targeting ability by coating related cell membranes and exhibits outstanding inhibition of tumor growth and metastasis,as well as no noticeable side-effects during treatments.This simple small molecular self-assembled nano-prodrug provides a new reasonably designed modality for ferroptosis-combined chemotherapy.

新几内亚凤仙CCoAOMT和SAMS基因的克隆及表达分析

通过对新几内亚凤仙(Impatiens hawkeri)指甲花醚合成相关基因CCoAOMT和SAMS的克隆及表达分析,以期为解析其合成途径提供一定的理论依据。基于新几内亚凤仙指甲花醚含量测定和转录组测序数据分析,克隆到1个CCoAOMT和2个SAMS基因,将其命名为IhCCoAOMT、IhSAMS1和IhSAMS2。3种处理(光培养、暗培养和前体物DHNA (1,4-dihydroxy-2-naphthoate)诱导)均能促进指甲花醚积累,且暗培养处理最佳。IhCCoAOMT的cDNA全长为729 bp,编码242 aa,其蛋白具有AdoMet_MTases超家族结构域;IhSAMS1和IhSAMS2的cDNA全长分别为1179 bp和1173 bp,分别编码393 aa和391 aa,其蛋白均具有S-AdoMet_synt超家族结构域。系统进化分析表明,IhCCoAOMT与喜马拉雅凤仙亲缘关系最近;IhSAMS1与IhSAMS2可能为旁系同源。qRT-PCR分析表明,3个基因在新几内亚凤仙的3个处理及4个时期中均有表达,其中IhCCoAOMT在暗培养及前体物DHNA诱导中表达量整体上调,且暗培养第60天时表达量最高;IhSAMS1和IhSAMS2在暗培养及DHNA诱导中表达量整体下调,但IhSAMS1在DHNA诱导中表达量较高,IhSAMS2在暗培养中表达量较高。综上所述,推测IhCCoAOMT在指甲花醚处理后期起主要作用,IhSAMS1和IhSAMS2在其早期起主要作用,且IhSAMS2作用更为显著。

结合SAM视觉分割模型与随机森林机器学习的无人机影像盐沼植被“精灵圈”提取

“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素在色彩信息和外形特征上差异较小,如何从二维影像中智能精准地识别“精灵圈”像素并对识别的单个像素形成个体“精灵圈”是目前的技术难点。本文提出了一种结合分割万物模型(Segment Anything Model,SAM)视觉分割模型与随机森林机器学习的无人机影像“精灵圈”分割及分类方法,实现了单个“精灵圈”的识别和提取。首先,通过构建索伦森-骰子系数(S?rensen-Dice coefficient,Dice)和交并比(Intersection over Union,IOU)评价指标,从SAM中筛选预训练模型并对其参数进行优化,实现全自动影像分割,得到无属性信息的分割掩码/分割类;然后,利用红、绿、蓝(RGB)三通道信息及空间二维坐标将分割掩码与原图像进行信息匹配,构造分割掩码的特征指标,并根据袋外数据(Out of Bag,OOB)误差减小及特征分布规律对特征进行分析和筛选;最后,利用筛选的特征对随机森林模型进行训练,实现“精灵圈”植被、普通植被和光滩的自动识别与分类。实验结果表明:本文方法“精灵圈”平均正确提取率96.1%,平均错误提取率为9.5%,为精准刻画“精灵圈”时空格局及海岸带无人机遥感图像处理提供了方法和技术支撑。

Efficient conversion of lignin waste and self-assembly synthesis of C@MnCo_(2)O_(4)for asymmetric supercapacitors with high energy density

Lignin waste from the papermaking and biorefineries industry is a significantly promising renewable resource to prepare advanced carbon materials for diverse applications,such as the electrodes of supercapacitors;however,the improvement of their energy density remains a challenge.Here,we design a green and universal approach to prepare the composite electrode material,which is composed of lignin-phenolformaldehyde resins derived hierarchical porous carbon(LR-HPC)as conductive skeletons and the self-assembly manganese cobaltite(MnCo_(2)O_(4))nanocrystals as active sites.The synthesized C@MnCo_(2)O_(4)composite has an abundant porous structure and superior electronic conductivity,allowing for more charge/electron mass transfer channels and active sites for the redox reactions.The composite shows excellent electrochemical performance,such as the maximum specific capacitance of~726 mF cm^(-2)at 0.5 mV s^(-1),due to the significantly enhanced interactive interface between LR-HPC and MnCo_(2)O_(4)crystals.The assembled all-solid-state asymmetric supercapacitor,with the LR-HPC and C@MnCo_(2)O_(4)as cathode and anode,respectively,exhibits the highest volumetric energy density of 0.68 mWh cm^(-3)at a power density of 8.2 mW cm^(-3).Moreover,this device shows a high capacity retention ratio of~87.6%at 5 mA cm^(-2)after 5000 cycles.

基于SAM&ImageJ图像处理的堆石混凝土坝层面露石率研究

堆石混凝土坝层面的外露块石为上下层提供了重要的啮合作用,其投影面积比例是科学评价层间抗剪性能的重要指标。采用国际最新Meta AI模型segment anything model(SAM)对层面外露堆石进行自动图像分割,并基于ImageJ软件对SAM识别后的图片进行再加工与图像计算,利用平滑、差分算法、中值滤波等方法精准标定外露堆石,二值化后计算得到层面露石率。结果表明:SAM图像预分割可识别约90%的外露堆石,经过ImageJ二次图像处理后可有效提高小粒径堆石的识别精度,对比手动标注结果误差在±3%以内。以贵州省两座水库的工程应用为例,对浇筑仓面进行分区预处理,结果发现靠近上游、中部、下游不同区域的露石率差别较大,计算得到的层面露石率以10%~30%居多,其中堆石入仓运输通道区域的露石率较低。研究内容与结论可为堆石混凝土结构层间界面抗剪力学性能和大坝蓄水安全稳定的研究提供参考与借鉴。

基于SDP和改进SAM⁃MobileNetv2的滚动轴承故障诊断方法研究

针对传统的滚动轴承故障诊断方法难以准确高效的实现故障分类,提出了一种融合对称点模式(Symmetrized Dot Pattern,SDP)和改进SAM⁃MobileNetv2的滚动轴承故障分类方法。首先,将轴承振动信号通过SDP算法转化为含有丰富特征信息的二维图像。然后,将二维图像输入到改进SAM⁃MobileNetv2网络模型中,对故障特征信息进行提取和分类。在改进SAM⁃MobileNetv2网络中,使用自适应激活函数ACON(Activate or not)对SAM⁃MobileNetv2中的ReLU6激活函数进行替换,提高模型分类性能。最后,将本模型与多种网络模型做对比。试验结果表明,本模型可以准确高效地实现对滚动轴承故障的分类,使用凯斯西储大学轴承故障数据的准确率为99.5%,使用渥太华大学轴承故障数据的准确率为97.2%。

Analysis on the formation principle and present situation of nano phase state of multi-component self-assembly of traditional Chinese compound medicine decoction

Traditional Chinese medicine decoction is a complex polydispersed phase system containing real solution,colloid solution,emulsion and suspension.The compound decoction of traditional Chinese medicine has complex components,including saponins,alkaloids,polysaccharides,flavonoids,amino acids and so on,which can be self-assembled to form gels,fibers,micelles,vesicles and so on.The self-assembled nano-phase not only neutralizes the single drug and reduces the toxicity and side effects,but also has its own pharmacological effects,which complement each other to achieve synergistic effect,so as to achieve the role of drug supplement,which is of research significance.The formation principle,solubilization and synergism principle and characterization method of multi-component self-assembly of traditional Chinese medicine compound decoction are discussed in this paper.

基于时差提示SAM的遥感变化检测

遥感变化检测作为观测和分析地表变化的重要手段,以双时相图像为输入,旨在预测变化发生的“位置”。近期出现的基础模型,例如SAM模型(Segment Anything Model),展现出强大的通用性和泛化能力,有望为变化检测任务提供更为有效的解决方案。然而,由于遥感图像的特殊成像特性,它们在许多遥感应用中的直接使用往往效果不佳。此外,SAM模型最初被设计用于分割单时相图像,其通过人工添加点或框的提示来实现,但这种过于直观的方法在处理双时相图像输入时并不适用。为了应对上述挑战,本文提出了一种基于时差提示SAM的遥感变化检测方法(TDPS),充分发挥SAM模型强大的视觉识别能力,以改进对遥感图像的变化检测。具体而言,本文首先在SAM骨干网络中引入了低阶可微调参数,以减轻自然图像到遥感图像上的域偏移。其次,本文设计了时相差异提示生成器,通过将双时相图像的特征与查询嵌入一起优化,得到适用于变化检测任务的提示向量。最后,大量实验证明了本文方法的有效性,在两个常用的变化检测数据集LEVIR-CD和WHU-CD上取得了最先进的性能,F1指标相比于最先进方法分别提升了1.4%和2.5%。

温度梯度下SAMs膜对油滴迁移行为控制及减摩效果试验研究

采用自组装单分子膜技术以及局部紫外光刻技术相结合的方法,制备了条形润湿性表面,用于解决由温度梯度作用造成的油滴热毛细迁移而导致的接触区域乏油问题.使用高速摄像机以及接触角测量仪,对温度梯度下,液体石蜡在所制备表面与原始不锈钢表面上的迁移速度、迁移距离及接触角进行了对比.结果表明:在不锈钢表面设计的条形图案能够有效地抑制油滴的热毛细迁移现象.将制备表面进一步应用于摩擦磨损试验中,发现所制备表面能使液体石蜡在摩擦的过程中始终汇聚在摩擦接触区,减缓了接触区乏油,能够明显减少摩擦磨损.此研究为解决由油滴热毛细迁移而引起的接触区乏油问题提供了新思路和新方法.

SAM Era:Can It Segment Any Industrial Surface Defects?

Segment Anything Model(SAM)is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model.Due to its superior performance in general object segmentation,it quickly gained attention and interest.This makes SAM particularly attractive in industrial surface defect segmentation,especially for complex industrial scenes with limited training data.However,its segmentation ability for specific industrial scenes remains unknown.Therefore,in this work,we select three representative and complex industrial surface defect detection scenarios,namely strip steel surface defects,tile surface defects,and rail surface defects,to evaluate the segmentation performance of SAM.Our results show that although SAM has great potential in general object segmentation,it cannot achieve satisfactory performance in complex industrial scenes.Our test results are available at:https://github.com/VDT-2048/SAM-IS.