This work reports the design and experimental characterization of two femtosecond-laser-inscribed fiber Bragg grating (FBG) vibration sensors based on direct polydimethylsiloxane (PDMS) encapsulation, for low- and ultralow-frequency monitoring. FBG accelerometers for this regime have so far relied on rigid mass-spring structures with inertial masses; embedding the grating directly in a soft elastomer, trading absolute sensitivity for low mass and conformability, had not been examined for acceleration sensing. Two comparable gratings were embedded in PDMS beams of different geometry, a reference structure (L = 50 mm, h = 7 mm) and a longer, thinner structure (L = 60 mm, h = 4 mm). The longer, thinner sensor exhibited a mean sensitivity of 0.0763 +/- 0.0056 nm/(m/s2) at 10 Hz, 105.5% higher than the reference (0.0371 +/- 0.0010 nm/(m/s2)); the measured resonance frequencies (80.6 Hz and 42.0 Hz) matched finite element predictions within 0.31% and 6.03%. Sensitivity was statistically flat across 0.3-20 Hz for both sensors, with a noise-floor-derived dynamic range of 35.0 dB and 42.7 dB. Benchmarked against recent rigid low-frequency FBG accelerometers, the devices did not reach the highest absolute sensitivity, yet both recovered a periodic excitation down to 0.001 Hz, one decade below the lowest frequency reported among them, with signal-to-noise ratios of 6-18 dB. These results demonstrate that a conformable, low-mass PDMS-embedded FBG sensing element recovers a known periodic excitation down to 0.001 Hz, one order of magnitude below the lowest frequency reported for the metallic massspring devices it is benchmarked against, complementing rather than replacing rigid mass-spring devices where the highest absolute sensitivity is required.
Pavement construction relies heavily on natural aggregates and conventional stabilizers such as Portland cement (PC) and lime, contributing to resource depletion, high energy demand, and significant greenhouse gas emissions. Geopolymerization, the alkaline activation of aluminosilicate materials, offers a more sustainable alternative. Mine tailings (MT), abundant mining by-products rich in silica and alumina, are attractive geopolymer precursors, although their reuse is constrained by variable composition and potential toxicity. In mining-intensive countries like South Africa, copper, cassiterite, and gold MT represent promising resources. This review critically synthesizes current knowledge on the physicochemical properties, mechanical performance, and environmental implications of geopolymerized MT in pavement applications. Unlike previous reviews that consider MT or geopolymers in isolation, it establishes a pavement-engineering–driven framework linking MT characteristics, geopolymer chemistry, and pavement performance requirements across laboratory, environmental, and field scales. The reviewed evidence indicates that MT-based geopolymers can achieve higher strength and superior durability than PC or lime-stabilized bases while reducing CO2 emissions by up to 80
TiO2 nanoparticles can exhibit benefits in various areas, including physics, chemistry, biology, the environment, and agriculture, among others, for the advancement of the world. In this research work, TiO2 nanomaterials are fabricated using a green synthesis technique called mechanical attrition and analysis of their properties for CO gas sensors. The structural study confirmed the stable tetragonal crystal structure for TiO2 samples, reducing the crystallinity and forming defects at higher milling times. The morphological analysis showed the transformation of larger grains (more than 1 μm) to nano-sized grains (around 100 nm) after 10 h of the milling process, verifying the formation of nanoparticles. The decrease in oxygen content (at.
Mangroves are vulnerable to rising sea levels and have shown two main mechanisms for adaptation: (1) vertical adjustments related to feedback between plant growth, inundation, and sediment deposition, and (2) horizontal movement to occupy adjacent ecosystems. In this study, the landward migration of mangroves on the Yucatán Peninsula (YP) was characterized for the period 1984–2024. A hybrid classification approach was used to construct coverage maps of mangroves, other vegetation types, and associated land uses. Overlay analysis revealed landward migration in four sub-regions. The total mangrove area identified with landward migration corresponded to 1754.38 ha, distributed in 79 polygons. The average area and distance of landward migration was 22 ha and 178.1 m, respectively. The average landward migration rate observed was 0.5 ha year−1, with an average landward migration speed of 4.47 m year−1. Landward migration occurred in an altitudinal range between − 5.93 and 6.77 m asl, where 50
Tropical cyclones (TCs) pose significant risks due to their associated hazards, including powerful winds, inland and coastal flooding, and wind waves. However, more reliable TC records are required to ensure a robust statistical analysis for risk assessment. To overcome this limitation, researchers have developed methods to generate synthetic tropical cyclones (STCs) that provide a larger sample size of occurrences at specific locations. This study compares STC databases from different sources such as Massachusetts Institute of Technology (MIT), Columbia HAZard model (CHAZ), Synthetic Tropical cyclOne geneRation Model (STORM), and Deltares with historical TCs from the International Best Track Archive for Climate Stewardship (IBTrACS) on a basin-wide scale in the North Atlantic Basin. The aim is to assess the effectiveness of STCs in replicating crucial historical tropical cyclones parameters for risk analysis and to identify potential biases in the STC generation models. The comparison uses a hexagonal mesh to evaluate characteristics such as maximum winds, translation speed, and residence time. The study acknowledges the validation paradox arising from the limited IBTrACS data at specific locations that make it difficult to rigorously validate the accuracy of STCs in those areas and from systematic differences across the STC datasets. Despite the historical TCs database limitation, comparing STC with IBTrACS characteristics remains the only viable method for assessing biases in STC generation models. The evaluated STCs reveal spatial bias patterns, which may indicate deficiencies in the underlying hazard models. Identifying and describing these biases aim to guide the use of these events and highlight key aspects for further development in STC generation methods.
