点击 最 下方 "在看"和" "并分享，"关注"材料汇 添加 小编微信 ，遇见 志同道合 的你 正文 Part 研究背景与意义 01 ● 公众号·材料汇 1 5G通信技术的特点 Non-ionising radiation'- 2.4GHZ VS. 5GHZ 波长 700 MHz 2.6 3.4 - 3.8 2.4 5G 扩展频 0 随着信息技术的发展,实时传输的数据 量日益增加，低频通信无法满足海量数 据实时传输。提高信号传输容量和速率 最有效的方式是使用高频电磁波。 C—信号传输速率 香农定理: B—信道带宽，与频率正相关 C = B ×log2 S/N S/N—信噪比 2 公众号·材料汇 5G通信技术的特点 大数据容量 5G 日2 智慧城市 智能驾驶 低时延 特 点 高传输速率 虚拟增强现实 远程医疗 ·公众号·材料汇 1 高频通信对电路基材的要求- 低介申 通信技术和通信设备的发展对设 备电路基材提出了更高的要求 信号传输损耗与 信号传输延时与 介电性能的关系 介电常数的关系 Tax - Dk L = K × (f/C) × Df × VDk 5G通信是依靠器件实现无线电磁波 L 传输损耗; K 常数 ...

Group 1 - The 2025 Yinchuan New Materials Industry Matching Conference and Western Materials and Energy Academic Conference was held on August 9, 2023, with nearly 300 participants from academia and industry [1] - The conference focused on new materials and renewable energy, discussing energy conversion and storage materials, advanced functional materials, and green low-carbon materials [1] - The event provided a platform for communication among researchers and professionals in the materials science field, aiming to enhance academic research and application levels [1] Group 2 - The New Materials Industry Matching Conference has been successfully held six times, facilitating in-depth exchanges between over 260 experts and more than 600 industry representatives [2] - The conference has led to the implementation of 13 research cooperation projects, including 2 national key projects and 3 regional key projects, contributing to the high-quality development of Ningxia's new materials industry [2] - Starting in 2026, the conference will be renamed to "JMST - Frontiers in Materials Science and Technology Innovation Seminar and Ningxia Materials and New Energy Industry-University-Research Matching Conference" [2]

Core Viewpoint - The article provides a comprehensive guide on material selection based on various mechanical properties such as Young's modulus, strength, and cost, emphasizing the importance of choosing the right materials for specific applications. Group 1: Young's Modulus and Density - When hard materials are needed, such as for top beams or bicycle frames, materials at the top of the chart should be selected [2] - For low-density materials, such as packaging foam, materials on the left side of the chart are recommended [2] - Finding materials that are both rigid and lightweight is challenging, and composite materials are often a good choice [3] Group 2: Young's Modulus and Cost - For hard materials, the top materials in the chart should be chosen for applications like top beams and bicycle frames [14] - For low-cost materials, those on the left side of the chart are preferred [14] - If a cheap and hard material is required, materials in the upper left corner of the chart, mostly metals and ceramics, should be selected [15] Group 3: Strength and Density - The strength indicated in the chart refers to tensile strength, with ceramics showing compressive strength [26] - High-strength and low-density materials are located in the upper left part of the graph [26] - Strength is a critical indicator of a part's ability to resist failure under load [26] Group 4: Strength and Cost - The strength indicated is tensile strength, except for ceramics which indicate compressive strength [38] - Many applications require materials with high strength, such as screwdrivers and seat belts, but these materials are often expensive [38] - Only a few materials can meet both strength and cost requirements, typically found in the upper left part of the chart [38] Group 5: Strength and Toughness - The strength indicated is tensile strength, while ceramics indicate compressive strength [50] - Typically, materials with poor toughness also have low strength; increasing strength may reduce toughness [50] - Strength measures a material's ability to resist external forces, while toughness measures its ability to absorb energy before failure [50] Group 6: Strength and Elongation at Break - Ceramics have very low elongation at break (<1%); metals have moderate elongation (1-50%); thermoplastics have high elongation (>100%) [61] - Rubber exhibits long-term elastic elongation, while thermosetting polymers have low elongation (<5%) [61] Group 7: Strength and Maximum Working Temperature - The chart applies to components used in environments where working temperatures exceed room temperature, such as cookware and automotive parts [73] - Polymers have lower maximum working temperatures, metals have medium, and ceramics can withstand very high temperatures [73] Group 8: Specific Strength and Specific Stiffness - Specific strength is defined as strength divided by material density, while specific stiffness is stiffness divided by material density [84] - High strength and high stiffness usually coexist, as they largely depend on the bonding forces between atoms [84] Group 9: Resistivity and Cost - The chart is primarily for selecting materials that require low prices and good electrical insulation or conductivity [97] - Good electrical conductors are typically good thermal conductors, while good electrical insulators are good thermal insulators [97] Group 10: Recyclability and Cost - The chart identifies materials' recyclability features, especially for expensive and recyclable materials [108] - Metals are particularly suitable for recycling due to ease of sorting and remelting, while ceramics are rarely recycled [108] Group 11: Production Energy Consumption and Cost - The energy consumed in producing a material is a factor in raw material costs, with most materials located in the low-cost/low-energy or high-cost/high-energy quadrants [121] - Metals often require significant energy for extraction, such as aluminum production consuming a substantial portion of total energy in the U.S. [123]

Core Insights - The concept of "Physical AI" is introduced as a new wave in artificial intelligence, allowing machines to learn from physical laws and data without manual coding [3][4] - Physical AI can significantly enhance efficiency in various industries, such as aerospace and pharmaceuticals, by reducing design and testing time and costs [3][4] - Investment opportunities are identified in companies focused on computing power, industrial software, and materials science that can leverage Physical AI [3][4][5] Computing Power - Physical AI requires higher computing power than traditional AI, making companies like NVIDIA, which produces GPUs, central to this trend [3][4] - Domestic companies involved in computing clusters are also expected to benefit in the long term [3] Industrial Software - The integration of Physical AI with engineering simulation software is anticipated to create new opportunities for companies in the industrial simulation and CAE software sectors [3][4] Materials Science - Companies engaged in the development of new batteries and specialty materials can potentially double their research efficiency by utilizing Physical AI [3][4] Investment Horizon - The implementation of Physical AI is expected to take three to five years or longer, suggesting it is more suitable for medium to long-term investment strategies [4][5] - Historical context is provided, indicating that early investors in AI who focused on computing and algorithm companies have seen significant returns [4][5]

Market Overview - The projected market size for various industries by 2025 includes: Chemical at $58.182 billion, Pharmaceutical at $16.232 billion, New Energy at $23.310 billion, Semiconductor at $7.189 billion, Alloy at $3.349 billion, and Display at $1.955 billion [7] AI Penetration Rates - AI penetration rates in different sectors are expected to increase significantly, with Chemical reaching 3.86%, Pharmaceutical at 7.77%, New Energy at 4.82%, Semiconductor at 15.18%, Alloy at 2.53%, and Display at 7.20% by 2025 [7] Company Profiles - **JingTai Technology**: Founded in 2015, focuses on first-principles computing, AI, and robotics for drug discovery and new materials development, backed by investors like Tencent and Sequoia [10] - **Deep Principle Technology**: Established in 2024, aims to apply AI and quantum chemistry in chemical materials research, focusing on generating target chemical materials and reactions [53] - **Molecular Heart**: Founded in 2022, specializes in protein structure prediction and molecular modeling, with backing from notable investors [10] - **Deep Cloud Intelligence**: Founded in 2020, focuses on AI and automation for new material synthesis, providing digital solutions for the energy sector [43] Investment Trends - Investment in companies like **Hongzhiwei** and **Deep Principle Technology** shows a trend towards funding in AI-driven material research and development, with significant rounds of financing reported [11][25][53] Product and Service Offerings - Companies are offering a range of products including high-throughput material screening systems, AI-driven design platforms, and simulation software for material properties [31][41][45] Collaborations and Partnerships - Collaborations with major institutions and companies such as Huawei, CATL, and various universities highlight the industry's focus on leveraging academic and corporate partnerships for innovation [14][28] Industry Challenges - The industry faces challenges such as high development costs and the need for advanced computational tools to overcome limitations in material design and testing [47][49]

Core Viewpoint - Ferroelectric materials are crucial for various electromechanical devices due to their excellent piezoelectric properties, enabling efficient conversion between electrical and mechanical energy [2][3][5]. Group 1: Development and Applications - Over the past century, a variety of ferroelectric materials have been developed, including lead zirconate titanate ceramics, lead-free ceramics, aluminum nitride films, and ferroelectric polymers based on polyvinylidene fluoride [2]. - These innovations have expanded the range of applications for ferroelectric materials and provided greater flexibility in device design, benefiting numerous piezoelectric devices such as cooling fans in smartphones and ultrasound transducers [2][5]. - The review paper published by Professor Li Fei highlights the reliance of small electromechanical devices, like speakers and motors in smartphones, on ferroelectric materials, which deform under an electric field [3]. Group 2: Research Progress and Future Directions - Recent research has focused on enhancing the piezoelectric performance of ferroelectric materials, proposing strategies to meet the growing demand for high-performance piezoelectric devices and systems [6]. - The review also emphasizes the need to consider the environmental impact of ferroelectric materials throughout their lifecycle, from raw material acquisition to manufacturing, usage, and disposal [6]. Group 3: Market Relevance - The current and emerging piezoelectric devices in the 3C (computer, communication, and consumer electronics) sector illustrate the diversity of piezoelectric applications, particularly in consumer electronics like smartphones [8].

Core Viewpoint - The development of a reverse design AI model for thermal radiation metamaterials by a research team at Shanghai Jiao Tong University significantly enhances the efficiency of material design, enabling rapid generation of numerous candidate designs for various applications [1][2]. Group 1: AI Model Development - The AI model can filter through over 50,000 design combinations in just three months, a process that would take an astronomical amount of time using conventional methods [1]. - The model is inspired by the three-dimensional topology of biological structures and utilizes a novel "three-plane modeling method" to accurately describe three-dimensional structural units [1]. - A comprehensive database containing 57,110 sets of data has been established, linking materials, superstructures, and spectral performance [1]. Group 2: Practical Applications - The team validated the model's effectiveness by designing and experimentally verifying four types of thermal radiation metamaterials for specific applications, including flexible films, coatings, and patches [2]. - In outdoor tests, these metamaterials demonstrated excellent self-cooling effects, with single-band selective metamaterials showing surface temperatures 2.5°C and 5.3°C lower than broadband metamaterials and commercial white paint, respectively [2]. - The experimental results indicate the model's potential applications in building energy efficiency and mitigating urban heat island effects [2].

Core Viewpoint - The Shanghai Jiao Tong University team has achieved a significant breakthrough in the field of thermal radiation metamaterials by constructing an AI model for reverse design, which allows for the mass generation of candidate designs for thermal radiation metamaterials, thus surpassing existing material design limitations [1] Group 1 - The breakthrough involves the development of an AI model that enables the reverse design of thermal radiation metamaterials [1] - The research results were published in the journal "Nature" on July 2 [1] - The new approach allows for the selection of the best candidates from a large pool of generated designs, enhancing the efficiency of material development [1]

Core Viewpoint - The article announces a training program focused on the application of artificial intelligence in materials science, emphasizing the importance of AI in driving innovation and industrial progress in the materials industry [1][2]. Summary by Sections Training Content - The program will cover various topics including: 1. New paradigms in materials science driven by AI 2. AI-enabled data acquisition, processing, and standardization in materials 3. AI's role in the discovery and design of new materials 4. AI applications in predicting material structures and properties 5. Use of AI in material characterization and testing 6. Multi-scale high-throughput computing in materials science 7. Automation in materials science experiments and design using AI 8. Core principles of AI-enabled materials science technologies 9. Applications of machine learning in materials science 10. Case studies of deep learning in materials science 11. Applications of reinforcement learning in key materials science technologies 12. Neuromorphic and brain-like computing applications in materials science 13. AI's contribution to intelligent manufacturing and industrialization of materials 14. Analysis of outstanding achievements in AI-enabled materials science [1][2]. Participants - The program is targeted at leaders, researchers, and technical personnel from enterprises, research institutes, and universities engaged in materials science, as well as individuals interested in the field [2]. Schedule and Location - The third session is scheduled from July 24 to 27, 2025, in Beijing (with online participation available) - The fourth session will take place from September 11 to 14, 2025, in Guangzhou (also with online participation) [2]. Invited Experts - Experts from renowned institutions such as the Chinese Academy of Sciences, Tsinghua University, Beijing University of Science and Technology, and Shanghai Jiao Tong University will be invited to teach [2]. Fees and Registration - The fee for participation is 4,980 yuan per person, which includes costs for experts, venue, meals, materials, and teaching services. Accommodation is available but at the participant's expense. The training is organized by Beijing Dingji Technology Consulting Co., Ltd. [3].

Group 1: Emerging Technologies - The World Economic Forum released the "Top 10 Emerging Technologies Report" for 2025, highlighting technologies that could address global challenges in the next 3 to 5 years [1] - Structural battery composite materials are set to revolutionize the transportation sector by combining mechanical load-bearing capabilities with electrochemical energy storage, potentially transforming electric vehicles and aircraft [1] - Salinity gradient power generation technology utilizes the difference in salinity between two water sources to generate electricity, marking a significant breakthrough in clean energy [2] - Innovations in nuclear energy, including small modular reactors, are driving a new wave of development aimed at reducing costs and optimizing designs, with the ultimate goal of achieving controlled nuclear fusion [3] Group 2: Healthcare Innovations - Scientists are transforming probiotics into micro "pharmaceutical factories," which could lead to more economical and sustainable disease treatment options, reducing production costs by 70% [4] - GLP-1 receptor agonists, initially developed for type 2 diabetes and obesity, show promising potential in treating neurodegenerative diseases like Alzheimer's and Parkinson's by reducing brain inflammation and clearing harmful proteins [5] - Smart biochemical sensors are capable of real-time monitoring of specific biochemical indicators, expanding their applications from diabetes management to food safety [6] Group 3: Agricultural and Environmental Technologies - New nitrogen fixation technologies are being developed to reduce energy consumption and greenhouse gas emissions associated with traditional nitrogen fixation processes, which currently consume 2% of global energy [7][8] - Nanozymes, synthetic materials mimicking natural enzymes, offer advantages such as stability and cost-effectiveness, with applications in water purification and cancer research [8] - Collaborative sensing technologies are reshaping urban management by enabling interconnected devices to optimize traffic flow and environmental monitoring [8] Group 4: Digital Security Technologies - Generative watermarking technology embeds invisible markers in AI-generated content to help distinguish authenticity, although challenges such as user manipulation and ethical dilemmas remain [9]