Sensors International最新文献_第2页

Multimetallic graphene-coated THz metasurface biosensor for high-sensitivity hCG detection in pregnancy testing: A simulation study 多金属石墨烯涂层太赫兹超表面生物传感器用于妊娠试验中hCG的高灵敏度检测：模拟研究

Sensors International

Pub Date : 2026-01-01 Epub Date: 2025-09-11 DOI: 10.1016/j.sintl.2025.100351

K. Vijayakumar , S. Subha , N.K. Anushkannan , Kumaravel Kaliaperumal , U. Arun Kumar

Conventional pregnancy testing methods face significant limitations including low sensitivity, cross-reactivity issues, and requirement for sophisticated laboratory equipment, particularly in resource-limited settings. This research introduces an innovative terahertz (THz) biosensor using a graphene-metallic hybrid metasurface architecture to improve pregnancy detection by optical sensing of human chorionic gonadotropin (hCG) indicators. The sensor demonstrates remarkable performance with a maximum sensitivity of 1000 GHz/RIU achieved at the optimal resonant frequency of 0.309 THz within the 0.1–0.55 THz frequency band, corresponding to a refractive index of 1.343 RIU. The frequency-dependent sensitivity analysis reveals that the maximum sensitivity of 1000 GHz/RIU is achieved at 0.309 THz, where the electromagnetic field enhancement reaches its peak value. This optimal operating point corresponds to the fundamental resonance mode of the hybrid metasurface structure, where the coupling between the central graphene resonator and the surrounding metallic rings creates the strongest field localization. The sensitivity decreases progressively at frequencies away from this resonant peak, with values of 500 GHz/RIU at 0.310 THz and 200 GHz/RIU at 0.311 THz, demonstrating the critical importance of precise frequency tuning for optimal sensor performance. Comparative analysis shows competitive or superior performance against existing biosensor designs, offering significant potential for point-of-care pregnancy testing applications with enhanced sensitivity, real-time detection capability, and reduced sample preparation requirements.

传统的妊娠检测方法面临着显著的局限性，包括低灵敏度、交叉反应性问题，以及对复杂实验室设备的要求，特别是在资源有限的情况下。本研究介绍了一种创新的太赫兹（THz）生物传感器，该传感器采用石墨烯-金属混合超表面结构，通过光学传感人类绒毛膜促性腺激素（hCG）指标来改进妊娠检测。在0.1-0.55 THz频段内，在0.309 THz的最佳谐振频率下，传感器的最大灵敏度达到1000 GHz/RIU，对应的折射率为1.343 RIU。频率相关的灵敏度分析表明，在0.309太赫兹时达到1000 GHz/RIU的最大灵敏度，此时电磁场增强达到峰值。这个最佳工作点对应于混合超表面结构的基本共振模式，其中中心石墨烯谐振器与周围金属环之间的耦合产生最强的场局域化。在远离该谐振峰的频率处，灵敏度逐渐降低，在0.310太赫兹处灵敏度为500 GHz/RIU，在0.311太赫兹处灵敏度为200 GHz/RIU，这表明精确的频率调谐对于优化传感器性能至关重要。对比分析显示，与现有的生物传感器设计相比，具有竞争力或优越的性能，具有更高的灵敏度、实时检测能力和更低的样品制备要求，为即时妊娠检测应用提供了巨大的潜力。

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引用次数: 0

Selective detection of toxic Au3+ using novel dicyano-[5]helicene-based fluorescence sensor: Applications in real samples and human neuroblastoma cells 利用新型二矢亚诺-[5]螺旋烯荧光传感器选择性检测毒性Au3+：在真实样品和人类神经母细胞瘤细胞中的应用

Sensors International

Pub Date : 2026-01-01 Epub Date: 2026-01-07 DOI: 10.1016/j.sintl.2026.100372

Raveewan Kittiyaphong , Nirawit Kaewnok , Pramsak Patawanich , Pattarapapa Janthakit , Vinich Promarak , Anyanee Kamkaew , Pattanawit Swanglap , Jitnapa Sirirak , Natdhera Sanmanee , Krit Setthakarn , Nantanit Wanichacheva

Gold is a valuable noble metal having widespread applications across various fields. However, Au³⁺ accumulation in the human body and the environment can pose serious health and ecological risks. Therefore, effective methods for Au³⁺ detection must be developed. Herein, a fluorescence sensor was successfully synthesized, to the best of our knowledge, as the first dicyano-[5]helicene-based sensor (MP) for Au³⁺ detection. Spectroscopic techniques and single-crystal X-ray analysis were used to confirm the molecular structure and photophysical properties of MP. The sensor exhibited a large Stokes shift of 111 nm and a high fluorescence quantum yield. Upon exposure to Au³⁺ MP displayed a “turn-off” fluorescence response, which indicates its high selectivity toward Au³⁺ over other competing metal ions along with excellent sensitivity. The detection limit of MP reached 4.2 ppb, which is lower than the guideline value for Au³⁺ toxicity in freshwater environments. The sensing mechanism for Au³⁺ detection was proposed to rely on the alkynophilicity of Au³⁺, activating the triple bond and inducing hydration of the alkyne moiety. This mechanism was supported by Fourier transform infrared spectroscopy, ¹H nuclear magnetic resonance spectroscopy, high-resolution mass spectrometry, and molecular modeling. This sensor demonstrated high potential for qualitative fluorometric assays of Au³⁺ levels in diverse real samples, such as environmental water, drinking water, tap water, fertilizer solutions, cosmetic products, and human neuroblastoma cells. In addition, it could be applied for the quantitative detection of gold nanorods and further developed into a paper-based test strip for onsite Au³⁺ screening.

金是一种贵重的贵金属，在各个领域都有广泛的应用。然而，Au3+在人体和环境中的积累会造成严重的健康和生态风险。因此，必须开发有效的Au3+检测方法。本文成功合成了荧光传感器，据我们所知，这是第一个用于检测Au3+的基于二氨基[5]螺旋烯的传感器（MP）。利用光谱技术和单晶x射线分析证实了MP的分子结构和光物理性质。该传感器具有111 nm的大斯托克斯位移和高荧光量子产率。暴露于Au3+ MP后显示出“关闭”荧光响应，表明其对Au3+的选择性高于其他竞争金属离子，同时具有优异的灵敏度。MP的检出限为4.2 ppb，低于淡水环境中Au3+毒性指导值。提出了Au3+检测的传感机制是依靠Au3+的亲炔性，激活三键，诱导炔部分水化。傅里叶变换红外光谱、1H核磁共振光谱、高分辨率质谱和分子模型支持了这一机制。该传感器在各种实际样品（如环境水、饮用水、自来水、肥料溶液、化妆品和人类神经母细胞瘤细胞）中Au3+水平的定性荧光测定中表现出很高的潜力。此外，它还可以应用于金纳米棒的定量检测，并进一步发展成为用于现场Au3+筛选的纸质测试条。

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引用次数: 0

A novel DHP-NH2 fluorescent dye for the determination of water content in organic solvents, rice, and aspirin: DFT calculations 一种新型DHP-NH2荧光染料，用于测定有机溶剂、大米和阿司匹林中的水分含量：DFT计算

Sensors International

Pub Date : 2026-01-01 Epub Date: 2025-08-09 DOI: 10.1016/j.sintl.2025.100349

Kittiporn Nakprasit , Panyakorn Taweechat , Pornthep Sompornpisut , Mongkol Sukwattanasinitt , Waroton Paisuwan , Anawat Ajavakom

A novel aminodihydropyridine derivative (DHP-NH₂) was synthesized from hydrazine via a tandem cyclotrimerization of methyl propiolate. DHP-NH₂ distinguishably exhibited either strong fluorescence in aprotic solvents or weak fluorescence in protic solvents. Its fluorescence quenching in THF and MeCN was quantitatively determined in the presence of water content, demonstrating the limit of detection (LOD) of 0.036 %wt and 0.014 %wt in THF and MeCN, respectively. The critical hydrogen-bonding interactions between water molecules and the amino group of DHP-NH₂ were found to stabilize its excited state, supporting a quenching mechanism as confirmed by DFT/TDDFT calculations. To apply this DHP-NH₂ probe for on-site analysis, smartphone-based photography together with the ImageJ program was employed for the moisture detection with the LOD of 0.28 %wt in MeCN. Moreover, the trace amount of moisture in organic solvents and solid samples (rice and aspirin) was successfully detected by using this developed method. In addition, we successfully immobilized the DHP derivative onto the cellulose paper to be used as a portable test strip for determining water content in MeCN by naked-eye detection.

以丙酸甲酯为原料，通过串联环三聚化反应合成了一种新的氨基二氢吡啶衍生物（DHP-NH2）。DHP-NH2在非质子溶剂中表现出强荧光，在质子溶剂中表现出弱荧光。在有水存在的情况下，定量测定了其在THF和MeCN中的荧光猝灭，其在THF和MeCN中的检出限（LOD）分别为0.036% wt和0.014% wt。发现水分子与DHP-NH2氨基之间的临界氢键相互作用稳定了其激发态，支持DFT/TDDFT计算证实的猝灭机制。为了将该DHP-NH2探针应用于现场分析，采用智能手机摄影结合ImageJ程序对men中的水分进行检测，LOD为0.28% wt。此外，该方法还成功地检测了有机溶剂和固体样品（大米和阿司匹林）中的微量水分。此外，我们成功地将DHP衍生物固定在纤维素纸上，作为裸眼检测MeCN含水量的便携式试纸条。

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引用次数: 0

Development of a standalone software application for the simulation and optimization of surface plasmon resonance-based biosensors 开发用于模拟和优化基于表面等离子体共振的生物传感器的独立软件应用程序

Sensors International

Pub Date : 2026-01-01 Epub Date: 2025-10-16 DOI: 10.1016/j.sintl.2025.100352

Innocent Kadaleka Phiri , Mohssin Zekriti , Tijani Bounahmidi

Surface Plasmon Resonance (SPR) has emerged as a powerful biosensing technique, enabling real-time, label-free detection of target biomolecules with high sensitivity. However, the design and optimization of SPR-based biosensors remain challenging, requiring both theoretical expertise and access to specialized simulation tools. The growing demand for these biosensors highlights the need for advanced features and capabilities, such as automated computation of key biosensing metrics, user interactivity, real-time visualization, and dataset generation for machine learning applications. These capabilities are often lacking or scattered across different existing simulation platforms. To address this gap, we developed ‘SPR-Soft’, a new, standalone, PC-based software application for SPR biosensor simulation and optimization. Based on the Transfer Matrix Method (TMM), SPR-Soft features a user-friendly graphical interface that allows real-time input adjustments, live visualization of reflectivity/transmissivity curves, and automated computation of key performance metrics including sensitivity, Full Width at Half Maximum (FWHM), Detection Accuracy (DA), Figure of Merit (FoM), minimum reflectivity (R_min), and Field Enhancement (FE). Additionally, the software includes a dataset generation module to support machine learning-based applications in biosensor design. SPR-Soft's accuracy was validated through comparison with published simulation data and benchmarked against existing tools. A case study is also presented, demonstrating the software's capabilities by optimizing a gold-silver alloy-based SPR biosensor, achieving enhanced performance: sensitivity of 342°/RIU, FoM of 53.12/RIU, and R_min of 0.017 a.u. This development addresses long-standing limitations in biosensor modelling tools, improves research efficiency, enhances accessibility for non-expert users, and ultimately, supports the United Nations Sustainable Development Goals (SDGs #3 and #9).

表面等离子体共振（SPR）已经成为一种强大的生物传感技术，能够实时、无标记地检测目标生物分子，具有很高的灵敏度。然而，基于spr的生物传感器的设计和优化仍然具有挑战性，既需要理论知识，也需要专业的模拟工具。对这些生物传感器不断增长的需求凸显了对先进特性和功能的需求，例如关键生物传感指标的自动计算、用户交互性、实时可视化和机器学习应用的数据集生成。这些功能通常缺乏或分散在不同的现有仿真平台上。为了解决这一差距，我们开发了“SPR- soft”，这是一种新的，独立的，基于pc的软件应用程序，用于SPR生物传感器模拟和优化。基于传递矩阵法（TMM）， SPR-Soft具有用户友好的图形界面，允许实时输入调整，反射率/透射率曲线的实时可视化，以及关键性能指标的自动计算，包括灵敏度，半最大全宽度（FWHM），检测精度（DA），优异值（FoM），最小反射率（Rmin）和场增强（FE）。此外，该软件还包括一个数据集生成模块，以支持生物传感器设计中基于机器学习的应用。通过与已发布的仿真数据进行比较，并与现有工具进行基准测试，验证了SPR-Soft的准确性。本文还介绍了一个案例研究，通过优化基于金银合金的SPR生物传感器，展示了该软件的功能，实现了更高的性能：灵敏度为342°/RIU， FoM为53.12/RIU， Rmin为0.017 a.u。该开发解决了生物传感器建模工具长期存在的局限性，提高了研究效率，增强了非专业用户的可访问性，并最终支持了联合国可持续发展目标（SDGs #3和#9）。

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引用次数: 0

Design and implementation of a microwave microstrip sensor with convolutional neural network for real-time milk spoilage detection 基于卷积神经网络的微波微带传感器的设计与实现

Sensors International

Pub Date : 2026-01-01 Epub Date: 2025-10-16 DOI: 10.1016/j.sintl.2025.100353

Ali Khoshchehre , Mohammad Amir Sattari , Umer Hameed Shah , Gholam Hossein Roshani

Milk spoilage detection plays a pivotal role in safeguarding food safety and minimizing waste within the dairy sector, although conventional chemical assays remain labor-intensive, invasive, and expensive. The present investigation introduces a non-invasive microwave microstrip sensor coupled with a convolutional neural network (CNN) for real-time assessment of milk spoilage progression. The sensor, modeled and optimized using Advanced Design System (ADS) software to exhibit dual passbands (1807–2466 MHz and 3604–4426 MHz), was fabricated on an RT/Duroid 4003 substrate and evaluated using 10 commercial milk samples (3 % fat) procured sequentially over 10 days and maintained at 21 °C. Measurements of the S21 transmission parameter (101 frequency points per spectrum, with five replicates per sample yielding 50 spectra in total) demonstrated a substantial amplitude disparity, notably at 2166 MHz, where the difference between the freshest (day 10) and most spoiled (day 1) samples attained 7.02 dB—equivalent to approximately 105 times the mean standard deviation (0.067 dB)—facilitating robust differentiation of dielectric alterations attributable to microbial degradation. A one-dimensional CNN was trained on preprocessed spectral data augmented fivefold with white Gaussian noise using five variable standard deviations (σ = 0.20–0.60 dB) to simulate real-world measurement fluctuations, expanding the dataset from 50 to 250 spectra and attaining a training accuracy of 95.5 % and a validation accuracy of 90 %. This hybrid methodology surpasses traditional approaches in terms of rapidity and non-destructiveness, providing a viable framework for milk quality surveillance with applicability to other perishable commodities.

尽管传统的化学检测仍然是劳动密集型、侵入性和昂贵的，但牛奶腐败检测在保障食品安全和最大限度地减少乳制品行业的浪费方面发挥着关键作用。本研究介绍了一种非侵入性微波微带传感器与卷积神经网络（CNN）相结合，用于实时评估牛奶变质过程。该传感器使用Advanced Design System （ADS）软件建模和优化，具有双通带（1807-2466 MHz和3604-4426 MHz），在RT/Duroid 4003衬底上制造，并使用10个商业牛奶样品（3%脂肪）进行评估，这些样品在10天内连续获得，并保持在21°C。S21传输参数的测量（每个频谱101个频率点，每个样品5次重复，总共产生50个光谱）显示了巨大的幅度差异，特别是在2166 MHz，其中最新鲜（第10天）和最变质（第1天）样品之间的差异达到7.02 dB -相当于平均标准偏差（0.067 dB）的105倍-促进了可由微生物降解引起的介电变化的强大区分。利用5个变量标准差（σ = 0.20-0.60 dB）加5倍高斯白噪声的预处理光谱数据对一维CNN进行训练，模拟实际测量波动，将数据集从50个光谱扩展到250个光谱，训练精度达到95.5%，验证精度达到90%。这种混合方法在快速和非破坏性方面超越了传统方法，为牛奶质量监测提供了一个可行的框架，适用于其他易腐商品。

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引用次数: 0

High-sensitivity fiber optic sensor for carbon dioxide monitoring in IoT-Enabled biogas systems 用于物联网沼气系统中二氧化碳监测的高灵敏度光纤传感器

Sensors International

Pub Date : 2026-01-01 Epub Date: 2026-02-07 DOI: 10.1016/j.sintl.2026.100377

P. Thaisongkroh , V. Onpol , S. Pullteap

This research presents a high-sensitivity fiber optic refractometric sensor integrated with Internet of Things technology for real-time monitoring of carbon dioxide in biogas applications, addressing critical industrial requirements for continuous gas composition analysis while providing remote accessibility and electromagnetic immunity. The sensing mechanism employs polyhexamethylene biguanide (PHMB) functionalized coating applied to de-cladded fiber regions, enhancing selectivity toward carbon dioxide detection through evanescent wave interactions at the core-cladding interface, where carbon dioxide-induced refractive index variations produce measurable optical intensity changes in single-mode fiber architecture. Experimental validation demonstrates robust performance across carbon dioxide concentrations ranging from 34.7% to 93.3%, achieving a high sensitivity of 0.196%/s with response and recovery times of approximately 120 s and 180 s, respectively, while multiple measurement cycles confirm excellent repeatability and stability without baseline drift or signal degradation. The integrated Internet of Things platform enables seamless data acquisition and web-based visualization through secure interfaces, facilitating real-time monitoring with 3-s update intervals and maintaining an average latency of 70 ms for immediate response capabilities. Microstructural analysis confirms chemical stability and adhesion integrity of the PHMB coating following extended carbon dioxide exposure, ensuring long-term operational reliability for continuous monitoring applications. This fiber optic-Internet of Things integration demonstrates significant advantages for industrial gas sensing applications, including compact design, remote monitoring capabilities, and enhanced safety protocols that support process optimization in biogas production facilities while addressing electromagnetic interference concerns common in industrial environments, indicating strong feasibility for commercial deployment in high-sensitivity real-time monitoring systems.

本研究提出了一种集成了物联网技术的高灵敏度光纤折射传感器，用于实时监测沼气应用中的二氧化碳，解决了连续气体成分分析的关键工业要求，同时提供远程访问和电磁抗扰性。传感机制采用聚六亚甲基双胍（PHMB）功能化涂层，将其应用于脱包层光纤区域，通过芯-包层界面上的倏逝波相互作用增强对二氧化碳检测的选择性，其中二氧化碳诱导的折射率变化在单模光纤结构中产生可测量的光强度变化。实验验证表明，在34.7%至93.3%的二氧化碳浓度范围内，该方法性能稳定，灵敏度高达0.196%/s，响应时间和恢复时间分别约为120秒和180秒，而多个测量周期证实了出色的重复性和稳定性，没有基线漂移或信号退化。集成的物联网平台通过安全接口实现了无缝的数据采集和基于web的可视化，促进了3-s更新间隔的实时监控，并保持了70毫秒的平均延迟，以实现即时响应能力。显微结构分析证实了PHMB涂层在长时间暴露于二氧化碳后的化学稳定性和粘附完整性，确保了连续监测应用的长期运行可靠性。这种光纤与物联网的集成为工业气体传感应用展示了显著的优势，包括紧凑的设计、远程监控能力和增强的安全协议，支持沼气生产设施的过程优化，同时解决工业环境中常见的电磁干扰问题，表明在高灵敏度实时监控系统中进行商业部署的可行性很强。

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引用次数: 0

High-sensitivity terahertz metasensor for cervical cancer Diagnosis: Graphene modulation and XGBoost-Assisted optimization 用于宫颈癌诊断的高灵敏度太赫兹元传感器：石墨烯调制和xgboost辅助优化

Sensors International

Pub Date : 2026-01-01 Epub Date: 2025-08-17 DOI: 10.1016/j.sintl.2025.100350

Vaijayanthimala J , Vaishnavi K , Arun Kumar U , Dhivya R

Cervical cancer remains a major cause of mortality, particularly in low-resource settings where traditional cytology-based screening faces challenges such as limited infrastructure and trained personnel. To address this, we present a terahertz (THz) graphene-enhanced metasurface biosensor enabling rapid, label-free detection of cervical cancer biomarkers without complex sample preparation or expensive labs. Using finite element method (FEM) simulations, we demonstrate that tuning graphene's chemical potential from 0.1 to 0.9 eV significantly modulates peak absorption from 0.223 to 1.316, providing a wide dynamic range for sensitive detection across varying sample concentrations. The sensor exhibits robust angular stability, with absorption increasing from 0.546 to 1.306 as the incident light angle shifts from 0° to 80°, ensuring reliable performance without precise optical alignment. Refractive index sensing experiments reveal frequency shifts of 50 GHz and consistently high absorption (55.16 %–56.54 %), achieving a sensitivity of 300 GHz per refractive index unit (RIU) and a figure of merit of 12 RIU⁻¹. To enhance diagnostic accuracy, we integrated an XGBoost machine learning algorithm that analyzes the complex spectral data, achieving 86 % prediction accuracy with low error rates. This combination of advanced sensing and AI-assisted analysis offers a promising, cost-effective solution for cervical cancer screening in resource-limited environments.

宫颈癌仍然是导致死亡的一个主要原因，特别是在资源匮乏的环境中，传统的基于细胞学的筛查面临着基础设施和训练有素的人员有限等挑战。为了解决这个问题，我们提出了一种太赫兹（THz）石墨烯增强的超表面生物传感器，可以快速、无标记地检测宫颈癌生物标志物，而无需复杂的样品制备或昂贵的实验室。利用有限元方法（FEM）模拟，我们证明了将石墨烯的化学势从0.1至0.9 eV调节到0.223至1.316的峰值吸收，为不同样品浓度的敏感检测提供了广泛的动态范围。该传感器具有强大的角稳定性，当入射光角从0°变化到80°时，吸收从0.546增加到1.306，确保了可靠的性能，无需精确的光学对准。折射率传感实验显示频率漂移50 GHz，持续高吸收（55.16% - 56.54%），每个折射率单位（RIU）的灵敏度为300 GHz，品质系数为12 RIU−1。为了提高诊断的准确性，我们集成了一个XGBoost机器学习算法来分析复杂的光谱数据，实现了86%的预测准确率和低错误率。这种先进传感和人工智能辅助分析的结合为资源有限环境中的宫颈癌筛查提供了一种有前景的、具有成本效益的解决方案。

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引用次数: 0

Dual electrochemical approaches for rapid gonorrhea DNA diagnosis 双重电化学方法用于淋病DNA快速诊断

Sensors International

Pub Date : 2026-01-01 Epub Date: 2025-06-27 DOI: 10.1016/j.sintl.2025.100345

Abdulhadee Yakoh , Anaïs Charles , Panisak Boonamnaj , Sudkate Chaiyo , Sirirat Rengpipat

Gonorrhea, caused by Neisseria gonorrhoeae, requires rapid diagnostics, especially with the post-pandemic surge in cases. Traditional PCR methods need specialized equipment and trained personnel, underscoring the need for alternative tools. Electrochemical biosensors offer a sensitive, portable solution but have limited reporting for gonorrhea detection. This study develops two electrochemical methods: label-free (signal-off) and labeling (signal-on). The label-free approach uses two DNA sequences and the [Fe(CN)₆]^3-/^4- (potassium ferricyanide/ferrocyanide) redox indicator. The labeling method employs a molecularly amplified DNA sandwich assay with ferrocene-labeled helpers for signal amplification. The label-free method achieved a limit of detection (LOD) of 2.1 nM and a linear dynamic range (LDR) of 10–500 nM, while the labeling method showed an LOD of 4.8 pM and an LDR of 0.5–1000 nM. To enhance practicality, Near Field Communication (NFC)-enabled sensing was used during non-invasive urine sample testing, enabling real-time, wireless detection without sophisticated instruments. This confirmed the superior performance of the labeling method. Molecular dynamics simulations provided insights into structural dynamics, linking experimental data with computational models. This integrated approach highlights the importance of selecting methods based on sensitivity, cost, and ease of use, advancing gonorrhea DNA biosensing technologies.

由淋病奈瑟菌引起的淋病需要快速诊断，特别是在大流行后病例激增的情况下。传统的PCR方法需要专门的设备和训练有素的人员，因此需要替代工具。电化学生物传感器提供了一种敏感、便携的解决方案，但对淋病检测的报道有限。本研究开发了两种电化学方法：无标记（信号关闭）和标记（信号打开）。无标记方法使用两个DNA序列和[Fe(CN)6]3-/4-（铁氰化钾/亚铁氰化钾）氧化还原指示剂。标记方法采用分子扩增DNA夹心法，二茂铁标记辅助物用于信号扩增。无标记法的检出限（LOD）为2.1 nM，线性动态范围（LDR）为10 ~ 500 nM，而标记法的LOD为4.8 pM，线性动态范围（LDR）为0.5 ~ 1000 nM。为了提高实用性，在非侵入性尿样检测中使用了近场通信（NFC）传感技术，无需复杂的仪器即可实现实时无线检测。这证实了标记方法的优越性能。分子动力学模拟提供了对结构动力学的见解，将实验数据与计算模型联系起来。这种综合方法强调了基于灵敏度、成本和易用性选择方法的重要性，促进了淋病DNA生物传感技术的发展。

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引用次数: 0

Printing the future of strain measurement: Flexible sensors via additive manufacturing for wearables, robotics, and smart infrastructure 打印应变测量的未来：可穿戴设备、机器人和智能基础设施的增材制造柔性传感器

Sensors International

Pub Date : 2026-01-01 Epub Date: 2026-02-05 DOI: 10.1016/j.sintl.2026.100375

Shuvodeep De , Shuo Xu , Ya Tang , Amiee Jackson , Peter L. Wang , Shalini J. Rukmani , Eric MacDonald , Xianhui Zhao , Chad Duty , Alex Roschli , Gianni Stano , Gianluca Percoco , Adam Stevens , Yan Li

This review presents the evolution of strain sensor technologies from traditional bonded wire models to next-generation flexible designs enabled by advanced materials and additive manufacturing through 3D printing. It highlights breakthroughs in fabrication techniques particularly fused filament fabrication (FFF), direct ink writing (DIW), and vat photopolymerization (VPP) that address the limitations of conventional approaches, including complex multi-step processing and alignment challenges. The incorporation of novel materials such as conductive polymers and hybrid composites is shown to significantly enhance key performance parameters like sensitivity, mechanical durability, and strain sensing range. The convergence of cutting-edge manufacturing, material science, and computational modeling signals a paradigm shift in strain sensing, with broad implications for emerging applications in aerospace, biomedicine, soft robotics, and beyond. This work underscores the importance of continued interdisciplinary collaboration to fully realize the potential of flexible strain sensor technologies in adaptive, high-performance systems.

本文介绍了应变传感器技术的演变，从传统的键合线模型到下一代柔性设计，这些设计是由先进材料和3D打印的增材制造实现的。它强调了制造技术的突破，特别是熔丝制造（FFF），直接墨水书写（DIW）和还原光聚合（VPP），这些技术解决了传统方法的局限性，包括复杂的多步骤加工和校准挑战。新型材料如导电聚合物和混合复合材料的掺入可以显著提高关键性能参数，如灵敏度、机械耐久性和应变传感范围。尖端制造、材料科学和计算建模的融合标志着应变传感的范式转变，对航空航天、生物医学、软机器人等领域的新兴应用具有广泛的影响。这项工作强调了持续跨学科合作的重要性，以充分实现柔性应变传感器技术在自适应高性能系统中的潜力。

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引用次数: 0

Simulation-driven dual-band Graphene–Silver terahertz metasurface biosensor integrated with machine learning for mode-resolved hemoglobin detection 模拟驱动的双波段石墨烯银太赫兹超表面生物传感器与机器学习集成，用于模式分辨血红蛋白检测

Sensors International

Pub Date : 2026-01-01 Epub Date: 2025-11-04 DOI: 10.1016/j.sintl.2025.100357

Gunasekaran Thangavel , V. Joseph Michael Jerard , K. Manivannan , A.N. Sasikumar , Vairaprakash Selvaraj , Manjunathan Alagarsamy

Accurate hemoglobin measurement is essential for diagnosing hematological disorders and monitoring cardiovascular health. This study introduces a terahertz metasurface biosensor combined with machine learning algorithms for rapid and non-invasive hemoglobin detection in clinical settings. The metasurface exhibits dual-band resonance in 2 THz regions, achieving a sensitivity of 450 GHz/RIU within a refractive index range of 1.34–1.43 RIU, with corresponding frequency shifts of 40 GHz and 30 GHz. Machine learning models, including Random Forest, Support Vector Machines, and Neural Networks, enhance the sensor's analytical capability. Across four clinical categories—normal, mild anemia, moderate anemia, and severe anemia—the models attain 96.5 percent classification accuracy, with recall and precision scores above 0.94. Ensemble learning reduces the root mean square error to 0.28 g/dL, while denoising methods increase the signal-to-noise ratio by 16.3 dB. The biosensor supports real-time analysis, requires minimal sample volume, and eliminates complex preparation, making it suitable for continuous hemoglobin monitoring and cardiovascular health management.

准确的血红蛋白测量对于诊断血液病和监测心血管健康至关重要。本研究介绍了一种结合机器学习算法的太赫兹超表面生物传感器，用于临床环境中的快速非侵入性血红蛋白检测。该超表面在2太赫兹区域呈现双频共振，在1.34-1.43 RIU的折射率范围内实现了450 GHz/RIU的灵敏度，相应的频移为40 GHz和30 GHz。机器学习模型，包括随机森林、支持向量机和神经网络，增强了传感器的分析能力。在四个临床类别中——正常、轻度贫血、中度贫血和严重贫血——该模型达到了96.5%的分类准确率，召回率和准确率得分高于0.94。集成学习将均方根误差降低到0.28 g/dL，而去噪方法将信噪比提高了16.3 dB。该生物传感器支持实时分析，需要最小的样本量，并消除了复杂的制备，使其适用于连续血红蛋白监测和心血管健康管理。

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