Advanced Materials Technologies
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2024
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IF 6.4
Fangzhen Hu,Chenkai Cao,Sancan Han,Ding Wang,Xi Chen
Transferring the concept of chemical-driven responses into artificial intelligence technology holds the key to mimicking olfactory for neuromorphic computing of chemical recognition. Currently, artificial olfactory systems are designed based on chemical sensor arrays. Time-dependent responses of the sensor arrays are processed by artificial neural networks for recognition. However, the sensors generate instantly volatile responses, and algorithms for the processing of the time-dependent responses have not been involved. The recognition accuracy and speed are severely impeded. A sensor array can only achieve an accuracy of 90% after at least 5 training epochs. Herein an artificial olfactory chemical-resistant synapse consisting of 3D hierarchical WO 3 @WO 3 nanofibers are demonstrated. The nanofibers exhibit persistent resistance responses through chemical exposures due to the strong chemisorption of water molecules. Typical synaptic behaviors including paired-pulse facilitation, long-term −1 short-term memory, and learning experience have been achieved. Next, a recurrent neural network that is committed to processing the time-dependent data is used to identify gas-phase chemicals of 3-hydroxy-2-butanone, triethylamine, and trimethylamine. Training-free gas recognition has been realized by a WO 3 @WO 3 nanofiber synapse only, in which the accuracy is above 90% at the first epoch. The results have great potential to satisfy stringent performance requirements on artificial perception systems.
研究领域:
材料科学
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神经形态计算
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人工智能
✓ 已验证
产品关联
DOI
高影响因子
Foods
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2024
·Vol. 13
·期 5
·793
·
IF 4.7
Mingming Zhao,Zhiheng You,Huayun Chen,Xiao Wang,Yibin Ying,Yixian Wang
Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography–mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric sensing combinatorics utilized 25 dyes sensitive to fruit volatile gases, generating a distinct scent fingerprint through cross-reactivity to diverse concentrations and varieties of gases. The unique scent fingerprints can be identified using DCNN. After capturing colorimetric sensor image data, the densely connected convolutional network (DenseNet) was employed, achieving an impressive accuracy rate of 97.39% on the validation set and 82.20% on the test set in assessing fruit ripeness. This fruit ripeness prediction system, coupled with a DCNN, successfully addresses the issues of complex pattern recognition and low identification accuracy. Overall, this innovative tool exhibits high accuracy, non-destructiveness, practical applicability, convenience, and low cost, making it worth considering and developing for fruit ripeness detection.
研究领域:
食品科学
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人工智能
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植物生理学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
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2024
·
IF 8.5
Maozhen Qu,Yingchao He,Weidong Xu,Da Liu,Changqing An,Shanming Liu,Guang Liu,Fang Cheng
The MobileNetV3-based improved sine-cosine algorithm (ISCA-MobileNetV3) was combined with an artificial olfactory sensor (AOS) to address the redundancy in olfactory arrays, thereby achieving low-cost and high-precision detection of mycotoxin-contaminated maize. Specifically, volatile organic compounds of maize interacted with unoptimized AOS containing eight porphyrins and eight dye-attached nanocomposites to obtain the scent fingerprints for constructing the initial data set. The optimal decision model was MobileNetV3, with more than 98.5% classification accuracy, and its output training loss would be input into the optimizer ISCA. Remarkably, the number of olfactory arrays was reduced from 16 to 6 by ISCA-MobileNetV3 with about a 1% decrease in classification accuracy. Additionally, the developed system showed that each online evaluation was less than one second on average, demonstrating outstanding real-time performance for ensuring food safety. Therefore, AOS combined with ISCA-MobileNetV3 will encourage the development of an affordable and on-site platform for maize quality detection.
研究领域:
食品科学
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人工智能
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传感器技术
✓ 已验证
DOI
PubMed
高影响因子
Nature Communications
·
2024
·Vol. 15
·期 1
·1-12
·
IF 14.7
Yin Xiaomeng,Zhang Hao,Qiao Xuezhi,Zhou Xinyuan,Xue Zhenjie,Chen Xiangyu,Ye Haochen,Li Cancan,Tang Zhe,Zhang Kailin,Wang Tie
The olfactory system can generate unique sensory memories of various odorous molecules, guiding emotional and cognitive decisions. However, most existing electronic noses remain constrained to momentary concentration, failing to trigger specific memories for different smells. Here, we report an artificial olfactory memory system utilizing conductive metal-organic frameworks (Ce-HHTP) that integrates sensing and memory and exhibits short- and long-term memory responses to alcohols and aldehydes. Experiments and theoretical calculations show that distinct memories are derived from the specific combinations of Ce-HHTP with O atoms in different guest. An unmanned aircraft equipped with this system realized the sensory memories in established areas. Moreover, the fusion of portable detection boxes and wearable flexible electrodes demonstrated the immense potential in off-site pollution monitoring and health management. This work represents an artificial olfactory memory system with two specific sensory memories under simultaneous conditions, laying the foundation for bionic design with qualities of human olfactory memory.
研究领域:
材料科学
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传感器
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生物医学工程
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环境监测
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健康管理
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产品关联
DOI
PubMed
高影响因子
ADVANCED MATERIALS
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2020
·Vol. 32
·期 14
·1907043
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IF 27.398
Guangfa Wang,Yushu Li,Zhenzhen Cai,Xincun Dou
The detection of ultralow or nonvolatile target analytes remains a significant challenge for artificial olfactory systems even after decades of development, which severely limits their widespread application. To overcome this challenge, an artificial olfactory system based on a colorimetric hydrogel array is constructed for the first time as a universal representative. As an effective extension of conventional artificial olfactory systems that integrates the merits of its predecessors, the proposed system accurately mimics olfactory mucosa and specific odorant binding proteins using hydrogels endowed with specific colorimetric reagents for the detection of hypochlorite, chlorate, perchlorate, urea, and nitrate. Therefore, the proposed system is capable of detecting and discriminating between these five airborne improvised explosive microparticulates with a detection limit as low as 39.4 pg. Additionally, the system demonstrates good reusability over ten cycles, rapid response time of ≈0.2 s, and excellent discrimination properties, despite significant variation. This proof-of-concept study on colorimetric artificial olfactory systems yields a novel strategy for the direct and discriminative detection of nonvolatile airborne microparticulates.
研究领域:
化学传感
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爆炸物检测
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分析化学
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材料科学
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生物技术
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产品关联
DOI
PubMed
高影响因子
ADVANCED FUNCTIONAL MATERIALS
·
2025
·
IF 19
Yaoxuan Cui,Feng Xu,Qingpeng Cao,Libin Qian,Xubin Zheng,Zefeng Shen,Fenni Zhang,Ming Chen,Xiao Liang,Di Wang
Biomimetic olfactory systems utilizing cross-sensitive sensing arrays can detect and discriminate odors–a fundamental perceptual modality lacking in most intelligent systems. A primary implementation barrier lies in the inherent complexity of fabricating heterogeneous sensing arrays. Herein, a laser-programmable direct-writing strategy for single-step fabrication of heterogeneous transition metal oxide (TMO) arrays from a single precursor is reported. By precisely tuning laser parameters, in situ synthesis and continuous compositional modulation of multinary TMO heterostructures are achieved within millisecond timescales. Each fine-tuned TMO demonstrates distinct selectivity, and their combination generates signature patterns for different volatile organic compounds and complex odor mixtures. Phase analysis in combined with density functional theory (DFT) calculations, elucidates that phase transitions among competing oxide phases follow Boltzmann distributions governed by transient thermal gradient dynamics. DFT calculations of the electronic density of states further reveal the gas sensing mechanisms of laser-direct-written TMO heterostructures with varying surface phases. In addition to the new insights into laser-induced phase transformations in TMOs, this work establishes a highly efficient and scalable strategy for fabricating olfactory sensing arrays.
研究领域:
材料科学
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传感器与传感技术
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激光加工
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纳米制造
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人工嗅觉
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计算材料科学
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）