Earthquakes are common geological disasters, and slopes under seismic loading can trigger coseismic landslides, while also becoming unstable due to accumulated damage caused by the seismic activity. Reinforced soil slopes are widely used as seismic-resistant geotechnical systems. However, traditional geosynthetics cannot sense internal damage in reinforced soil systems, and existing in-situ distributed monitoring technologies are not suitable for seismic conditions, thus limiting accurate post-earthquake stability assessments of slopes. This study presents, for the first time, the use of a batch molding process to fabricate self-sensing piezoelectric geogrids (SPGG) for distributed monitoring of soil behavior under seismic conditions. The SPGG's reinforcement and damage sensing abilities were verified through model experiments. Results show that SPGG significantly enhances soil seismic resistance and can detect soil failure locations through voltage distortions. Additionally, the tensile deformation of the reinforcement material can be quantified with sub-centimeter precision by tracking impedance changes, enabling high-precision distributed monitoring of reinforced soil under seismic conditions. Notably, when integrated with wireless transmission technology, the SPGG-based monitoring system offers a promising solution for real-time monitoring and early warning in road infrastructure, where rapid detection and response to seismic hazards are critical for mitigating catastrophic outcomes.

Arsenic (As) contamination of soil and groundwater poses a huge threat to world health by polluting food systems and causing major health problems, such as cancer, cardiovascular disease,skin lesions,kidney damage and other serious health problems. In recent years, there has been a lot of effort into designing, synthesizing, and developing chemosensors for arsenic species. Chemosensors containing heteroatoms such as oxygen, nitrogen, and sulfur provide coordination sites for metal ion detection. This study investigates the study of organic compounds for the fluorimetric and colorimetric detection of As ions in biological, agricultural, and environmental samples. These chemosensors are based on the skeleton of Schiff bases, thiourea, and pyridine. By comparing their identification capabilities, we hope to guide the development of future arsenic chemosensors that are efficient, sensitive, and selective, leading to more accessible methods for arsenic monitoring in a variety of real-world applications.

Rare earth elements (REEs) are a type of frequently reported emerging pollutant that affects plant growth. The harm caused by continuous exposure to low-dose REEs has rarely been studied. Quickly, accurately, and noninvasively monitoring the continuous influence of low-dose REEs on plant growth in situ is key to indicating and warning of its harm to plants and ecosystems. In this study, after continuous exposure to low-dose lanthanum [La(III), a REE] for 14 days, invisible damage occurred in leaf cells, and La accumulated continuously in the soybean plants (leaves > stems > roots > pods > seeds), causing potential human health risks. Two proteins [vitronectin-like protein (VN) and arabinogalactan proteins (AGP)] in leaf cells that bound La(III) were selected as biomarkers, and changes in these two proteins were detected by constructing dual-sensors in living leaf cells after continuous exposure to low-dose La(III) for 14 days. The results showed that the electrochemical outputs from leaf cells-the electron transfer resistance Ret(VN) and Ret(AGP)-were related to the damage indices such as MDA, chlorophyll content, electrolyte leakage, cell vitality, fresh and dry weight of leaves, and leaf area. Using this output, two warning intervals of visible damage were obtained: Ret(VN) was 8.53 %-47.22 %, and Ret(AGP) was 12.75 %-51.31 %. This study successfully demonstrated the real-time in situ detection of plant cell biomarker changes and invisible damage under low-dose La(III) exposure, providing methods for early warning monitoring of plant damage caused by low-dose continuous exposure to REEs.

Background: The detection of metal ions represents a critical analytical challenge due to their persistent environmental accumulation and severe toxic effects on ecosystems and human health. Even at trace concentrations, toxic metal ions can cause irreversible biological damage, necessitating the development of sensitive, selective, and rapid monitoring platforms. Advanced detection systems are urgently needed for environmental surveillance, industrial effluent control, and food/water safety applications where regulatory compliance and early warning capabilities are paramount. Results: This work presents an etching-based sensor array to identify and discriminate Pb2+, Hg2+, Cu2+, NO2-, Cr6+, and As3+ as hazardous ions. Au@Ag core@shell nanorods were utilized as sensing elements in different pH values in the presence of thiosulfate and thiourea as key elements in the oxidation of nanoparticles. Analytes' response patterns in the range of 1.0-30 mu M were analyzed via various methods, including heatmap, bar plot, and linear discriminant analysis (LDA), showing perfect discrimination. To ensure the sensor's applicability in real samples, we conducted meticulous testing on different sources, including tap water, well water, tilapia pond water, tomato soil extract, and urine samples. Significance: The sensor demonstrated excellent performance in classifying mixture samples and providing precise and accurate detection in real samples. This innovation offers a promising future for etching-based sensor arrays by utilizing core-shell nanoparticles as sensitive sensing elements and a significant contribution to global efforts in safeguarding public health and the environment from the threat of pollutants.

In this study, a flexible vertical graphene (VG) strain sensor was developed for monitoring geogrids deformation. The VG material was fabricated using radio frequency plasma-enhanced chemical vapor deposition, followed by spin-coating a polydimethylsiloxane (PDMS) solution for film curing, resulting in a flexible sensor within a PDMS substrate. The VG sensor was integrated with a wireless Bluetooth data acquisition system for automated and remote strain measurement. The stability performance of VG sensors was examined and effectively improved through cyclic loading tests in the laboratory. The drift ratio of electrical resistance before cyclic loading tests is 37.01%, which is reduced to only 0.5% after cyclic loading tests. Calibration tests show that the maximum measurement resolution and maximum measurement range of VG sensors is 0.7 micro-strain and 40000 micro-strain, respectively, indicating that VG sensors are highly effective for both high-strain resolution identification and large-strain measurement. Pullout tests demonstrate an average error of 5.67% between VG sensors and fiber Bragg grating sensors, suggesting that VG sensors are a promising alternative for large strain, wireless, and long-term geogrid monitoring.

Geosynthetics are widely used in civil engineering reinforcements owing to their high strength, acceptable toughness, and ease of transportation. However, traditional geosynthetics do not have the capability to monitor damage inside the soil. Therefore, in this paper, a new sensor-enabled piezoelectric geobelt (SPGB) is developed to measure the deformation of reinforced-soil structures. In-soil drawing tests are conducted to investigate the sensing performance of the SPGB. Variations in the voltage and impedance signals of the SPGB with the drawing displacement under different damage conditions are investigated. The results show that with the increase of drawing displacement, SPGB undergoes tensile deformation followed by pullout damage. In tensile deformation, the signal response of SPGB is related to strain. As the strain increases, the output voltage first increases and then decreases, and the impedance gradually decreases. In the pullout damage phase, the signal response of SPGB is related to the contact area between SPGB and soil. As the drawing displacement increases, the contact area between SPGB and soil gradually decreases, the output voltage gradually decreases, and the impedance gradually increases. Therefore, the SPGB, as a sensor- enabled geosynthetic, provides a reinforcing function to the soil body and simultaneously performs in-soil catastrophe identification.

Accurate determination of potassium ion (K+) concentration in fingertip blood, soil pore water, pipette solution, and sweat is crucial for performing biological analysis, evaluating soil nutrients levels, ensuring experimental precision, and monitoring electrolyte balance. However, current electrochemical K+ sensors often require large sample volumes and oversized reference electrodes, which limits their applicability for the aforementioned small-volume samples. In this paper, a K+ sensor integrated with a glass capillary and a spiral reference electrode was proposed for detecting K+ concentrations in small-volume samples. A K+-selective membrane (K+-ISM)/ reduced graphene oxide-coated acupuncture needle (working electrode) was spirally wrapped with a chitosangraphene/AgCl-modified Ag wire (reference electrode). This assembly was then inserted into a glass capillary, forming an anisotropic diffusion region of an annular cylindrical gap with width 410 mu m and height 20 mm. It was found that the capillary action of the glass capillary results in a raised liquid level of the sample inside it compared to that in the container, which promotes efficient contact between the small-volume sample and the K+ sensor. Besides, the formed anisotropic diffusion region limits the K+ diffusion from the bulk solution to the K+ISM, which leads to a larger potentiometric response of the K+-ISM. The glass capillary-assembled K+ sensor displays high performance, including a sensitivity 58.3 mV/dec, a linear range 10_ 5-10_ 1 M, and a detection limit 1.26 x 10_6 M. Moreover, it reliably determines K+ concentrations in artificial sweat of microliter volume. These results facilitate accurate detection of K+ concentration in fingertip blood, soil pore water, and pipette solution.

Field capacity (F.C.) is a crucial parameter in soil analysis, defining the limits of plant-available moisture content (M.C.). Integrating this concept into sensing technology provides valuable information for optimizing irrigation scheduling by determining the appropriate timing and quantity of irrigation, thereby preventing crop damage. This article presents a fractal-based microwave planar sensor (MPS) designed to estimate soil-moisture characteristics related to F.C. The proposed sensor utilizes a self-similar fractal (SSF) approach, operating in the ISM frequency band at 2.4 GHz, achieving high return losses of approximately -47.94 dB and enhanced sensitivity in material characterization. The sensor's performance is evaluated by varying F.C. values from 0% to 100% for similar textured soils with organic matter content (OMC) variations. The results demonstrate that variations in OMC significantly impact the dielectric properties of soil with moisture variations. Specifically, Sample-1, which has a low OMC, exhibits a lower epsilon(r) values than Sample-2 at all F.C. levels. The data suggest that the proposed sensor is sensitive to detect the impact of OMC variations on soil-moisture characteristics concerning F.C. A mathematical model has been formulated as a second-order polynomial equation, exhibiting coefficient of determination (R-2) value of 0.9771. This model has been developed specifically to evaluate F.C. values, demonstrating a strong correlation with the observed data. The performance of the proposed sensor confirms its potential application in agricultural fields for efficient irrigation scheduling and water resource conservation.

Structural colors are bright and possess a remarkable resistance to light exposure, humidity, and temperature such that they constitute an environmentally friendly alternative to chemical pigments. Unfortunately, upscaling the production of photonic structures obtained via conventional colloidal self-assembly is challenging because defects often occur during the assembly of larger structures. Moreover, the processing of materials exhibiting structural colors into intricate 3D structures remains challenging. To address these limitations, rigid photonic microparticles are formulated into an ink that can be 3D printed through direct ink writing (DIW) at room temperature to form intricate macroscopic structures possessing locally varying mechanical and optical properties. This is achieved by adding small amounts of soft microgels to the rigid photonic particles. To rigidify the granular structure, a percolating hydrogel network is formed that covalently connects the microgels. The mechanical properties of the resulting photonic granular materials can be adjusted with the composition and volume fraction of the microgels. The potential of this approach is demonstrated by 3D printing a centimeter-sized photonic butterfly and a temperature-responsive photonic material.

The stress state is the fundamental for evaluating the soil strength and stability, playing a crucial role. However, during the stress testing, local damage and other uncertain factors may lead to partial sensor data missing, causing the existing three-dimensional stress calculation method to fail. To accurately restore the soil stress state during data missing, a three-dimensional stress calculation method was developed based on three-dimensional stress testing principles, incorporating axisymmetric and one-dimensional compression characteristics. The three-dimensional stress, principal stress , the first invariant of stress I-1, the second in variant of stress J(2) and stress Lode angle of a sandy soil foundation under one-dimensional compression conditions with different data missing were calculated and compared to results with complete data. The results show that the method is highly accurate; as the load increases, the relative error decreases and converges. The principal stresses, the first invariant of stress I-1, the second invariant of stress J(2) and the stress Lode angle align with one-dimensional compression response, suggesting that this calculation method supports advanced data mining. This study offers a novel approach and a practical method for fully utilizing the test data.