先进材料技术先进材料技术是所有与技术相关的材料应用研究的新家，特别关注先进的设备设计、制造和集成，以及基于新型材料的新技术。它弥合了基础实验室研究与工业之间的差距。

Advanced Materials Technologies is an emerging journal focused on research related to the application of materials in technology, with a particular emphasis on the design, manufacturing, and integration of advanced equipment, as well as new technologies based on novel materials. The journal aims to bridge the gap between fundamental laboratory research and industrial applications.

《Advanced Materials Technologies》是一个专注于与技术相关的材料应用研究的新兴期刊，特别关注先进设备的设计、制造和集成，以及基于新型材料的新技术。该期刊旨在弥合基础实验室研究与工业应用之间的差距。

Advanced Materials Technologies

Abstract In this study, a tri‐layer Pt/Al/TiO x /HfO x /Al 2 O 3 /Pt memristor device is fabricated and analyze its electrical characteristics for reservoir computing and neuromorphic systems applications. This device incorporates an oxygen reservoir of a TiO x and a barrier layer of an Al 2 O 3 , enabling stable bipolar switching characteristics without the need for an electroforming process over 10 3 cycles. It also exhibits self‐rectifying properties under a negative bias. Based on these characteristics, it is investigated essential synaptic functions such as long‐term potentiation (LTP), long‐term depression (LTD), paired‐pulse facilitation (PPF), spike‐rate‐dependent plasticity (SRDP), spike‐duration‐dependent plasticity (SDDP), spike‐number‐dependent plasticity (SNDP), and spike‐amplitude‐dependent plasticity (SADP), to assess their suitability for neuromorphic applications that mimic biological synapses. Furthermore, utilizing the short‐term memory characteristics of the device, reservoir computing (RC) measurement from [0000] to [1111] in 4‐bit representation is conducted. This capability enables us to achieve a high accuracy of 95.5% in MNIST pattern recognition tasks. Lastly, the natural decay characteristics caused by oxygen ion migration in the device, examining the transition from short‐term to long‐term memory in image memorization tasks is explored. The potential for deployment in high‐density crossbar arrays by calculating the read margin based on the device I–V curve and programming scheme is also evaluated.

Hyeonseung Ji

Sungjoon Kim

Sungjun Kim

Self‐Rectifying Short‐Term Memory Phenomena Through Integration of TiOx Oxygen Reservoir and Al2O3 Barrier Layers for Neuromorphic System

通过集成TiOx氧储层和Al2O3阻挡层实现自整流短时记忆现象用于神经形态系统

Abstract Neuromorphic architectures are in the spotlight as promising candidates for substituting current computing systems owing to their high operation speed, scale‐down ability, and, especially, low energy consumption. Among candidate materials, memristors have shown excellent synaptic behaviors such as spike time‐dependent plasticity and spike rate‐dependent plasticity by gradually changing their resistance state according to electrical input stimuli. Memristor can work as a single synapse without programming support, which remarkably satisfies the requirements of neuromorphic computing. Here, the most recent developments in memristor‐based artificial synapses are introduced with their excellent synaptic behaviors accompanied with detailed explanation of their working mechanisms. As conventional memristive materials, metal oxides are reviewed with recent advancements in heterojunction technologies. An overview of organic materials is presented with their remarkable synaptic behaviors including their advantages of biocompatibility, low cost, complementary metal‐oxide semiconductor compatibility, and ductility. 2D materials are also introduced as promising candidates for artificial synapses owing to their flexibility and scalability. As emerging materials, halide perovskites and low‐dimensional materials are presented with their synaptic behaviors. In the last section, future challenges and research directions are discussed. This review article is hoped to be a guide to rational materials design for the artificial synapses of neuromorphic computing.

Sun Gil Kim

Ji Su Han

Hyojung Kim

Soo Young Kim

Ho Won Jang

Adv. Mater. Technol.

Recent Advances in Memristive Materials for Artificial Synapses

忆阻材料在人工突触中的最新进展

Abstract In today's era of big‐data, a new computing paradigm beyond today's von‐Neumann architecture is needed to process these large‐scale datasets efficiently. Inspired by the brain, which is better at complex tasks than even supercomputers with much better efficiency, the field of neuromorphic computing has recently attracted immense research interest and can have a profound impact in next‐generation computing. Unlike modern computers that use digital “0” and “1” for computation, biological neural networks exhibit analog changes in synaptic connections during the decision‐making and learning processes. Currently, the neuron node is usually implemented by dozens of silicon transistors, an approach that is energy‐intensive and nonscalable. In this paper, recent developments of synaptic electronics for the hardware implementation and acceleration of artificial neural networks will be discussed. Learning mechanisms and synaptic plasticity in the brain and the device level requirements for synaptic electronics will briefly be reviewed, emphasizing the nuance compared to requirements for nonvolatile memories. Several categories of emerging synaptic devices based on phase change memory, resistive memory, electrochemical devices, and 2D devices will be introduced, as well as their associated advantages, disadvantages, and future prospects.

Qingzhou Wan

Mohammad T. Sharbati

John R. Erickson

Yanhao Du

Feng Xiong

Emerging Artificial Synaptic Devices for Neuromorphic Computing

用于神经形态计算的新兴人工突触器件

Abstract A neuromorphic computing system may be able to learn and perform a task on its own by interacting with its surroundings. Combining such a chip with complementary metal–oxide–semiconductor (CMOS)‐based processors can potentially solve a variety of problems being faced by today's artificial intelligence (AI) systems. Although various architectures purely based on CMOS are designed to maximize the computing efficiency of AI‐based applications, the most fundamental operations including matrix multiplication and convolution heavily rely on the CMOS‐based multiply–accumulate units which are ultimately limited by the von Neumann bottleneck. Fortunately, many emerging memory devices can naturally perform vector matrix multiplication directly utilizing Ohm's law and Kirchhoff's law when an array of such devices is employed in a cross‐bar architecture. With certain dynamics, these devices can also be used either as synapses or neurons in a neuromorphic computing system. This paper discusses various emerging nanoscale electronic devices that can potentially reshape the computing paradigm in the near future.

Navnidhi K. Upadhyay

Hao Jiang

Zhongrui Wang

Shiva Asapu

Qiangfei Xia

J. Joshua Yang

Emerging Memory Devices for Neuromorphic Computing

新兴存储器件用于神经形态计算

Abstract In recent years, reconfigurable intelligent metasurface (RIS) has gained much attention due to its flexibility in modulating the phase, amplitude, and polarization of electromagnetic waves. It is widely used for wireless power and information transmission. However, RIS requires an external wired power supply or batteries to activate their modulation capabilities, as they do not generate the required energy themselves. This paper presents a self‐powered RIS that does not require an external wired power supply or batteries. The modulation capability is activated by harvesting radio frequency (RF) energy. The self‐powered RIS consists of an energy‐metatom, which is loaded with a PIN diode for flexible phase modulation. The inclusion of an absorber/energy port on the energy‐metatom allows for independent RF energy harvesting. The RIS consisting of energy‐metatoms can cut out all wired power supplies and batteries so that it can dynamically regulate electromagnetic waves by self‐powered means. As a proof‐of‐concept, a prototype composed of 10 × 10 energy‐metatoms is fabricated. The measured results are in good agreement with the simulated. This self‐powered RIS can be used as an “infinite‐lifetime” power supply for dynamic electromagnetic modulation in the RIS.

Yajie Mu

Mingyang Chang

Xin Wang

Jiaqi Han

Dexiao Xia

Xiangjin Ma

Haixia Liu

Long Li

A Self‐Powered Reconfigurable Intelligent Metasurface

自供电可重构智能超表面

Abstract Light‐based additive manufacturing methods are widely used to print high‐resolution 3D structures for applications in tissue engineering, soft robotics, photonics, and microfluidics, among others. Despite this progress, multi‐material printing with these methods remains challenging due to constraints associated with hardware modifications, control systems, cross‐contamination, waste, and resin properties. Here, a new printing platform coined Meniscus‐enabled Projection Stereolithography (MAPS) is reported, a vat‐free method that relies on generating and maintaining a resin meniscus between a crosslinked structure and bottom window to print lateral, vertical, discrete, or gradient multi‐material 3D structures with no waste and user‐defined mixing between layers. MAPS is compatible with a wide range of resins shown and can print complex multi‐material 3D structures without requiring specialized hardware, software, or complex washing protocols. MAPS's ability to print structures with microscale variations in mechanical stiffness, opacity, surface energy, cell densities, and magnetic properties provides a generic method to make advanced materials for a broad range of applications.

Puskal Kunwar

Arun Poudel

Ujjwal Aryal

Rui Xie

Zachary J. Geffert

Haven Wittmann

Daniel Fougnier

Tsung Hsing Chiang

Mathew M. Maye

Zhen Li

Pranav Soman

Multi‐Material Gradient Printing Using Meniscus‐enabled Projection Stereolithography (MAPS)

多材料梯度打印利用液面投影立体光刻技术（MAPS）

Abstract Polydimethylsiloxane (PDMS) is the most studied organosilicon polymer, finding broad applications in soft lithography of microfluidic devices, wearable electronics insulation, medical tubing, shielding coatings, and sealant elements. However, their cross‐linked forms swell severely in nonpolar oils due to the comparable solubility parameters. Here, an oil‐stable cross‐linked PDMS discovered by grafting flexible PDMS chains onto its polymeric networks is reported, which enables surface‐exposed chains with high dynamics. It is found that the synergetic effect of mobile nanopore filling and quasi‐liquid lubrication of PDMS brushes endows the cross‐linked matrix with a reduced oil absorption ratio of up to 91.7% without altering its surface composition. Moreover, stretching polymeric networks can be re‐arranged with an adequate grafting of PDMS chains, further enhancing the antipenetration performance. This discovery of the oil‐stable surface configuration of cross‐linked PDMS breaks the constraint of siloxane elastomers not being applicable in nonpolar soluble liquid, opening a new era of research in organosilicon chemistry.

Ruonan Hao

Hao Jing

Qing Wang

Yunlong Han

Zhiyuan Zhang

Zhiyuan Li

Chenglong Yin

Lizhong Li

Ranlong Zhang

Jining Sun

Lei Zhang

Tailoring Dynamic Chains of Cross‐Linked PDMS to Hinder Oil Penetration

定制交联PDMS动态链以阻碍油渗透

Abstract Organic electrochemical transistors (OECTs) are promising for neuromorphic architectures as they can generate multiple electrical states through the control of ion transport. However, conventional OECTs face limitations in mimicking a fully functional biological synapse due to their inability to achieve long‐term plasticity. In this study, a metal‐organic framework (MOF)‐enhanced OECT (MOECT) is fabricated by introducing MOF into the ion‐organic semiconductor (OSC) layer. MOFs are synthesized using the layer‐by‐layer (LBL) method, and additional cross‐linked OSC is introduced to prevent damage to the semiconductor layers during synthesis. The synthesized MOF layers hinder the rediffusion of ions in OECT, allowing ions to remain in the OSC for an extended period. In this study, the MOECT showed a change in current depending on the doping level, recording a current state 4.4 × 10 7 times higher than that of pristine OECT. Ultimately, the developed MOECTs are applied as synaptic transistors. MOECTs show 14% higher excitatory postsynaptic current (EPSC) after 130 s compared to pristine OECT, thereby strengthening the long‐term plasticity characteristics of neuromorphic devices. This method enhances the performance of synaptic transistors by introducing MOF, offering various possibilities through the selection of different MOF structures and ions, indicating it is a methodological approach with high potential.

Sung Moon Park

Yousang Won

Joon Hak Oh

Eun Kwang Lee

Enhancement of Synaptic Behavior in Organic Electrochemical Transistors via the Introduction of Layer‐by‐Layer Grown Metal‐Organic Framework

通过逐层生长金属有机框架增强有机电化学晶体管中的突触行为

Abstract The ability to achieve real‐time movement visualization in piezoresistive sensors remains a challenge. A visual piezoresistive sensor to perceive the intensity of mechanical stimuli and visible spatial location information is designed based on the mechanoluminescence material CaZnOS: Mn. The linearity, detection range, sensitivity, and stability of the sensor are tested, and the sensing mechanism of the sensor is discussed, and the relationship between force, resistance, and light intensity is established. A 5 × 5 sensor array is prepared to realize the visual detection of dynamic force trajectory, and combined with a convolutional neural network and random forest algorithm, the human writing number and pressure characteristics are recognized, and the writing path and handwriting of the robot arm are controlled by multi‐feature input. The experimental results show that the machine learning algorithm is very reliable with an accuracy of 98.33% for digit recognition and 97.21% for identity recognition. The visual piezoresistive pressure sensor provides a new idea for the visualization of flexible pressure and promotes the development of flexible sensors towards integration and intelligence.

Junwen Yu

Yun Liu

Quanwang Niu

Xiangfu Wang

Xiaohong Yan

Visual Piezoresistive Dual‐Response Sensor Based on CaZnOS: Mn Mechanoluminescence Materials

基于CaZnOS:Mn力致发光材料的视觉压阻双响应传感器

Abstract Nasopharyngeal cancer (NPC) is a challenging malignancy with a poor prognosis due to late diagnosis and resistance to conventional treatments. Nanotechnology offers promising solutions to address these limitations by enabling targeted drug delivery, enhancing therapeutic efficacy, and facilitating multimodal treatment strategies. By leveraging the unique properties of nanomaterials and nanocarrier systems, innovative approaches for early detection, targeted treatment, and multimodal therapy can be developed, paving the way for more effective and personalized management of NPC. This review provides a comprehensive overview of recent developments and applications of nanotechnology in NPC therapy, focusing on improving drug stability, biodistribution, and targeted delivery to NPC tumors.

Minmin Shao

Madineh Moradialvand

Francesca Della Sala

Zahra Khorsandi

Assunta Borzacchiello

Ana Cláudia Paiva‐Santos

Ehsan Nazarzadeh Zare

Kiavash Hushmandi

Xiangdong Wang

Siavash Iravani

Pooyan Makvandi

Yi Xu

Nanotechnology for Nasopharyngeal Cancer Therapy and Improving Drug Stability and Biodistribution

鼻咽癌治疗中的纳米技术及提高药物稳定性和生物分布

短名	Adv. Mater. Technol.
Journal Impact	6.23
国际分区	MATERIALS SCIENCE, MULTIDISCIPLINARY(Q1)
期刊索引	SCI Q1中科院 3 区

ISSN	2365-709X
h-index	91
国内分区	材料科学(3区)材料科学材料科学综合(3区)

涉及主题	材料科学工程类物理纳米技术化学量子力学复合材料有机化学光电子学计算机科学电气工程生物物理化学热力学化学工程医学光学操作系统冶金机械工程
出版信息	出版商: Wiley-Blackwell，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2016，原创研究文献占比: 85.86%，自引率:4.70%， Gold OA占比: 22.64%

Advanced Materials Technologies

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Self‐Rectifying Short‐Term Memory Phenomena Through Integration of TiOx Oxygen Reservoir and Al₂O₃ Barrier Layers for Neuromorphic System