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Recent Articles

21 pages, 2272 KiB

Open AccessArticle

Methane–Natural Clay Interfacial Interactions as Revealed by High-Pressure Magic Angle Spinning (MAS) Nuclear Magnetic Resonance (NMR) Spectroscopy

by Salim Ok, Julia M. Sheets, Susan A. Welch and David R. Cole

Fuels 2025, 6(1), 16; https://doi.org/10.3390/fuels6010016 (registering DOI) - 18 Feb 2025

Abstract

The current investigation aims to offer fundamental, molecular- to microscopic-level descriptions of methane gas inside natural source clay minerals. Texas montmorillonite (STx-1), Georgia kaolinite (KGa-2), and Ca²⁺-saturated Texas montmorillonite (Ca-STx-1, Ca-bentonite) were utilized as subsurface model clay minerals for elucidating nano-confinement behaviors of ¹³C-labeled methane gas. High-pressure magic angle spinning (MAS) nuclear magnetic resonance (NMR) was used to describe the interactions between methane and the clays by varying temperature and pressure. Proton-decoupled ¹³C-NMR spectra were acquired at 28.2 bar at 307 K, 32.6 bar at 346 K, 56.4 bar at 307 K, 65.1 bar at 346 K, 112.7 bar at 307 K, and 130.3 bar at 346 K. In the pure state, no significant thermal effect on the behavior of methane was observed. However, there was a perceptible variation in the chemical shift position of confined methane in the mixtures with the clays up to 346 K. Conversely, the ¹³C-NMR chemical shift of methane altered by varying pressure in a pure state, and the mixtures with clays, attributed to the interaction of methane with the clay surfaces or the nanopore network of the clay–silica mixed phase. Pressure-induced shifts in methane peak positions were observed: 0.25 ppm (28.2–56.4 bar) and 0.47 ppm (56.4–112.3 bar) at 307 K. For methane in a montmorillonite mixture, shifts were 0.32 ppm for bulk-like methane and 0.20 ppm for confined methane under similar conditions. At 346 K, increasing pressure from 65.1 to 130.3 bar caused shifts exceeding 0.50 ppm, with bulk-like methane showing a 0.64 ppm shift and confined methane a 0.57 ppm shift. There was only one ¹³C-NMR methane peak in the mixture with either kaolinite (KGa-2) or Ca-bentonite with line broadening compared to that of pure methane. Still, two peaks were observed in the mixture with STx-1, explained by the imbibition and mobility of methane in the pore network. Full article

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12 pages, 2700 KiB

Open AccessArticle

Synthesis of Thiazolo[5,4-d]thiazoles in an Eco-Friendly L-Proline–Ethylene Glycol Mixture

by Thiên Thuý Trang Nguyễn, Jean-François Longevial and Stéphanie Hesse

Molecules 2025, 30(4), 938; https://doi.org/10.3390/molecules30040938 (registering DOI) - 18 Feb 2025

Abstract

The hazardousness of solvents used in synthetic organic chemistry is well established. In this context, it is relevant to search for safer and greener alternatives. Within the last decades, deep eutectic solvents have been considered as possible and promising alternatives. Consequently, this study aims at using deep eutectic solvents to synthesize an emerging class of heteroaromatic compounds named thiazolo[5,4-d]thiazoles, for which interest is growing in the field of organics, electronics, and biology. To address this challenge, we developed a straightforward synthetic protocol consisting of condensing dithiooxamide and aromatic aldehyde in deep eutectic solvents to yield the desired thiazolo[5,4-d]thiazole without further purification. The first hit was obtained with the well-known L-proline:glycerol (1:2) mixture at 130 °C. However, dithiooxamide is degraded under these conditions, leading to the formation of impurities that may arise from the consequent amount of reactive L-proline. Reaction conditions were optimized by modifying the deep eutectic solvent nature and proportions, applying various temperatures, changing the activation and heating source, or adding auxiliary oxidants. As a consequence, eight thiazolo[5,4-d]thiazoles were synthesized in equal or better yields (20 to 75%) than the reported procedure under safe and eco-friendly conditions in a mixture of L-proline and ethylene glycol (1:50) with sodium metabisulfite at 130 °C for one hour. Full article

(This article belongs to the Special Issue 10th Anniversary of Green Chemistry Section)

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10 pages, 5842 KiB

Open AccessCase Report

Frontal Variant Alzheimer’s Disease or Primary Psychiatric Disorder? A Case Report

by Siew Fai Liew and Weishan Li

Reports 2025, 8(1), 24; https://doi.org/10.3390/reports8010024 (registering DOI) - 18 Feb 2025

Abstract

Background and Clinical Significance: In our case study, the patient experienced approximately a year-long delay in her diagnosis, where her initial diagnosis was mistakenly a primary psychiatric disorder, resulting in undue stress on her family. The aim of this case study is to raise awareness of frontal variant Alzheimer’s dementia (fvAD) and to increase knowledge amongst clinicians about this disorder, its management and the need for long-term follow up in specialized clinics. Case Presentation: In January 2023, a 56-year-old woman first presented with a 4-month history of worsening cognitive symptoms with considerable overlapping mood symptoms. Her Mini-Mental State Examination (MMSE) score was 20/28, whereas her Frontal Assessment Battery (FAB) score was 6/18. Upon neuropsychological evaluation, she demonstrated multidomain cognitive deficits, where impairments were most prominent in executive dysfunction, learning, memory and semantic fluency. There was evidence of progressive neurodegenerative changes, with brain MRI (April 2024) showing predominant bilateral frontal and parietal volume loss, sparing the occipital and temporal lobes. Amyloid positron emission tomography (PET) was diffusely positive. A diagnosis of fvAD (frontal variant Alzheimer’s dementia) with BPSD was made. Other differential diagnoses included a major neurocognitive disorder due to multiple etiologies (AD and dementia with Lewy bodies (DLB)), frontotemporal dementia (bvFTD), primary progressive aphasia (PPA) and the psychiatric disorder of pseudodementia secondary to a mood disorder. Conclusions: This case presented significant challenges given the atypical neuropsychological profile and the complexity of the symptom presentation with significant neuropsychiatric overlay. The preliminary research findings underscore the complexity of fvAD, warranting future research using fundamental approaches. Full article

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24 pages, 13219 KiB

Open AccessArticle

Deformation Mechanisms and Rainfall Lag Effects of Deep-Seated Ancient Landslides in High-Mountain Regions: A Case Study of the Zhongxinrong Landslide, Upper Jinsha River

by Xue Li, Changbao Guo, Wenkai Chen, Peng Wei, Feng Jin, Yiqiu Yan and Gui Liu

Remote Sens. 2025, 17(4), 687; https://doi.org/10.3390/rs17040687 (registering DOI) - 18 Feb 2025

Abstract

In high-mountain canyon regions, many settlements are located on large, deep-seated ancient landslides. The deformation characteristics, triggering mechanisms, and long-term developmental trends of these landslides significantly impact the safety and stability of these communities. However, the deformation mechanism under the influence of human engineering activities remains unclear. SBAS-InSAR (Small Baseline Subset-Interferometric Synthetic Aperture Radar) technology, UAV LiDAR, and field surveys were utilized in this study to identify a large ancient landslide in the upper Jinsha River Basin: the Zhongxinrong landslide. It extends approximately 1220 m in length, with a vertical displacement of around 552 m. The average thickness of the landslide mass ranges from 15.0 to 35.0 m, and the total volume is estimated to be between 1.48 × 10⁷ m³ and 3.46 × 10⁷ m³. The deformation of the Zhongxinrong landslide is primarily driven by a combination of natural and anthropogenic factors, leading to the formation of two distinct accumulation bodies, each exhibiting unique deformation characteristics. Accumulation Body II-1 is predominantly influenced by rainfall and road operation, resulting in significant deformation in the upper part of the landslide. In contrast, II-2 is mainly affected by rainfall and river erosion at the front edge, causing creeping tensile deformation at the toe. Detailed analysis reveals a marked acceleration in deformation following rainfall events when the cumulative rainfall over a 15-day period exceeds 120 mm. The lag time between peak rainfall and landslide displacement ranges from 2 to 28 days. Furthermore, deformation in the high-elevation accumulation area consistently exhibits a slower lag response compared to the tensile deformation area at lower zones. These findings highlight the importance of both natural and anthropogenic factors in landslide risk assessment and provide valuable insights for landslide prevention strategies, particularly in regions with similar geological and socio-environmental conditions. Full article

(This article belongs to the Special Issue Advances in Geological Hazard Characterization and Assessment: Merging Remote Sensing with Direct Surveys)

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13 pages, 2806 KiB

Open AccessArticle

GIS-Based Dashboards as Advanced Geospatial Applications for Climate Change Education and Teaching the Future

by Rafael De Miguel González, Juan Mar-Beguería, María Sebastián López and Ondrej Kratochvíl

ISPRS Int. J. Geo-Inf. 2025, 14(2), 89; https://doi.org/10.3390/ijgi14020089 (registering DOI) - 18 Feb 2025

Abstract

ArcGIS Dashboard technology allows for the integration and visualization of various maps, charts, and indicators within a single interface, using databases underneath a geographic information system. The two dashboards of the European project Teaching the Future address the dual objective of (i) cartographically displaying the evolution in four phases of climate change data experienced across the entire planet from 1954 to 2021 in vectorial cells measuring one degree of latitude by one degree of longitude and (ii) spatially representing three scenarios (low, medium, and high greenhouse gas emissions) up to the year 2100, showing potential temperature increases in those same cells. In addition to the maps, the different data and charts contribute to the understanding of anomalies relative to the average, the global increase in each selected area by zooming in on the map, and the evolution of both observed and projected data. Both dashboards represent an accurate and reliable treatment of the data, as well as a cartographic expression that is easy for map readers to understand, making them powerful resources for teaching climate change at any educational level, whether in higher education or schools and for a general audience. Full article

(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives)

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Abstract

Nasturtium officinale R. Br. (watercress) is an endangered species with valuable pharmaceutical, cosmetic, and nutritional properties. The purpose of this work was to evaluate the phytochemical profile and biological activity of extracts from microshoot cultures grown in PlantForm bioreactors and the parent plant material. After 20 days of cultivation, the cultures achieved the best results both in terms of key active ingredient content and biological activity. The glucosinolates (GSL) profile by the UHPLC-DAD-MS/MS method showed that the dominant compounds were glucobrassicin (493.00 mg/100 g DW, 10 days) and gluconasturtiin (268.04 mg/100 g DW, 20 days). The highest total polyphenol content (TPC) was obtained after a 20-day growth period (2690 mg GAE/100 g DW). Among polyphenols, the dominant compounds in the extracts from in vitro cultures were sinapinic acid (114.83 mg/100 g DW, 10 days) and ferulic acid (87.78 mg/100 g DW, 20 days). The highest antioxidant potential assessed by ABTS and DPPH assays was observed for ethanol extracts. The best results for inhibiting hyperpigmentation (18.12%) were obtained for ethanol extracts and anti-elastase activity (79.78%) for aqueous extract from N. officinale microshoot cultures. The extracts from microshoot cultures inhibited the growth of bacteria, including Cutibacterium acnes (MIC = 0.625 mg/mL). Antioxidant tests and the chelating capacity of iron ions Fe²⁺ of the face emulsion with N. officinale extracts showed higher results than the control. Full article

27 pages, 5597 KiB

Open AccessArticle

Smart Organization of Imbalanced Traffic Datasets for Long-Term Traffic Forecasting

by Mustafa M. Kara, H. Irem Turkmen and M. Amac Guvensan

Sensors 2025, 25(4), 1225; https://doi.org/10.3390/s25041225 (registering DOI) - 18 Feb 2025

Abstract

Predicting traffic speed is an important issue, especially in urban regions. Precise long-term forecasts would enable individuals to conserve time and financial resources while diminishing air pollution. Despite extensive research on this subject, to our knowledge, no publications investigate or tackle the issue of imbalanced datasets in traffic speed prediction. Traffic speed data are often biased toward high numbers because low traffic speeds are infrequent. The temporal aspect of traffic carries two important factors for low-speed value. The daily population movement, captured by the time of day, and the weather data, recorded by month, are both considered in this study. Hour-wise Pattern Organization and Month-wise Pattern Organization techniques were devised, which organize the speed data using these two factors as a metric with a view to providing a superior representation of data characteristics that are in the minority. In addition to these two methods, a Speed-wise Pattern Organization strategy is proposed, which arranges train and test samples by setting boundaries on speed while taking the volatile nature of traffic into consideration. We evaluated these strategies using four popular model types: long short-term memory (LSTM), gated recurrent unit networks (GRUs), bi-directional LSTM, and convolutional neural networks (CNNs). GRU had the best performance, achieving a MAPE (Mean Absolute Percentage Error) of 13.51%, whereas LSTM demonstrated the lowest performance, with a MAPE of 13.74%. We validated their robustness through our studies and observed improvements in model accuracy across all categories. While the average improvement was approximately 4%, our methodologies demonstrated superior performance in low-traffic speed scenarios, augmenting model prediction accuracy by 11.2%. The presented methodologies in this study are applied in the pre-processing steps, allowing their application with various models and additional pre-processing procedures to attain comparable performance improvements. Full article

(This article belongs to the Section Navigation and Positioning)

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