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可降解超低功耗人工突触
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长征十二号甲运载火箭首飞入轨成功,回收未取得预期成效;能自行修复的量子计算机问世丨智能制造日报
创业邦· 2025-12-24 03:25
3.【长征十二号甲运载火箭首飞入轨成功,回收未取得预期成效】2025年12月23日上午,长征十二 号甲(CZ-12A)运载火箭顺利完成首飞任务,成功实现二级入轨目标,不过其一级火箭回收验证未 取得预期成效。作为我国第二款首飞即尝试回收的液氧甲烷运载火箭,此次任务为后续相关技术迭代 优化积累了宝贵数据与实践经验。(腾讯网) 4.【韩国Innospace研制的运载火箭在巴西发射失败】巴西空军称,一枚韩国公司的运载火箭从巴西 阿尔坎塔拉发射中心升空后,不久即坠落并撞击地面。发射实况转播画面显示,巴西利亚时间周一晚 22:13,由韩国卫星发射服务公司Innospace研制的韩光-Nano火箭从发射台升空。但大约一分钟 后,画面中断。发射失利后,Innospace股价暴跌30%触及跌停。该公司未立即回复寻求置评的请 求。(搜狐) 更多智能制造产业资讯 …… 扫码可订阅产业日报 欢迎加入 睿兽分析会员 ,解锁 AI、汽车、智能制造 等相关 行业日报、图谱和报告 等。 1.【可降解超低功耗人工突触研制成功】韩国蔚山科学技术院科学家研发出一种完全可生物降解、性 能稳定且能耗极低的人工突触,其由贝壳、豆类和植物纤维等天然环保材 ...
可降解超低功耗人工突触研制成功将记忆保持时间延长至近百分钟
Xin Lang Cai Jing· 2025-12-22 23:28
Core Insights - A biodegradable artificial synapse has been developed by scientists at Ulsan Institute of Science and Technology, which can extend memory retention time to nearly 100 minutes, marking a significant advancement in sustainable neuromorphic technology [1][2] Group 1: Performance and Structure - The artificial synapse is made from natural, environmentally friendly materials such as shells, legumes, and plant fibers, and features a layered structure that mimics brain synapse functions [1] - The device operates with extremely low energy consumption, requiring only 0.85 femtojoules per signal transmission, significantly lower than the 2.4 to 24 femtojoules needed by natural brain synapses [1] Group 2: Biodegradability and Environmental Impact - The ion-active layers and the binding layer of the device can naturally decompose in soil within approximately 16 days, ensuring that no harmful substances are left behind after degradation [2] - This development addresses multiple challenges in creating neuromorphic devices that are low-power, stable, durable, and biodegradable, paving the way for environmentally safe interactions [2] Group 3: Application Potential - A simple robotic system has demonstrated the potential applications of the device, where it can amplify signals in response to heat, mimicking human reflex actions [2] - The research indicates the possibility of developing eco-friendly robots that can learn, respond to environmental stimuli, and safely decompose after completing their tasks [2]