Search Results (2,351)

In the future, agriculture will need better irrigation management options to produce more food and decrease its air and water pollution contributions. Hydroponic systems conserve water over field production, but up to 50% of applied irrigation could be discharged from open-drain systems. TVPD is an evapotranspiration model developed for greenhouse production, particularly for hydroponics. In this study, we calibrate and evaluate TVPD on environmental and evapotranspiration data from hydroponic tomato production and compare predictions to those of random forest (RF) and K-nearest neighbors (KNN). Using five time-ordered data splits, we sought to gauge prediction accuracy for data-limited settings, where the model needs to be implemented with the least calibration time possible, and we evaluated TVPD, RF, and KNN with a 10-fold cross-validation to assess overall model robustness. Across the five data splits, TVPD produced more accurate predictions (r²: 0.86 to 0.90; RMSE: 0.1739 to 0.5796 L tray⁻¹) than RF (r²: 0.06 to 0.73; RMSE: 0.7354 to 2.0505 L tray⁻¹) and KNN (r²: 0.06 to 0.59; RMSE: 0.7694 to 1.7090 L tray⁻¹). With calibration on only the first five days of data, TVPD was able to produce acceptable predictions (r² = 0.87, RMSE = 0.5796 L tray⁻¹). The mean r² for a 10-fold cross-validation was 0.81 for TVPD, 0.88 for RF and 0.81 for KNN, and mean RMSE values were slightly better for the cross-validation for RF (0.4970 L tray⁻¹) and KNN (0.4968 L tray⁻¹) than for TVPD (0.5922 L tray⁻¹). Overall, TVPD could be a useful model to predict evapotranspiration for irrigation management and could decrease the volume of discharged hydroponic waste solution. Full article

The continuous rise in carbon emissions poses a serious threat to the global climate, driving the urgent need for efficient CCUS technologies. Ionic liquids (ILs), with their negligible vapor pressure, excellent thermal stability, and tunable molecular structures, have emerged as promising materials for CO₂ capture. However, the high viscosity of bulk ILs severely restricts gas mass transfer. To overcome this limitation, integrating ILs with porous materials featuring large surface areas and well-defined pore structures has emerged as a synergistic strategy, combining the high CO₂ affinity and selectivity of ILs with the rapid mass transfer and structural stability of porous supports. This review systematically summarizes the CO₂ capture mechanisms and limitations of bulk ILs and further highlights recent advances in the design, synthesis, and applications of IL-based hybrid adsorbents. Particular attention is given to confinement-enhanced mechanisms, whereby nanoscale confinement fundamentally alters the physicochemical behavior of ILs, transforming them from disordered bulk liquids into ordered, interface-dominated systems. In addition, the life-cycle assessment and techno-economic analysis of IL hybrid systems are critically evaluated. Full article

Accurate estimation of the weighted mean temperature (Tm) is essential for retrieving precipitable water vapor (PWV) from ground-based Global Navigation Satellite System (GNSS) observations. Machine learning (ML) techniques excel in modeling nonlinear relationships among Tm time series, station geographic coordinates, and surface meteorological parameters, and recent studies have demonstrated that ML and neural network models outperform conventional linear Tm models. However, the full potential of surface meteorological measurements at GNSS stations for high-precision Tm retrieval remains to be fully explored. This study develops regional Tm empirical models using two ML methods—random forest (RF) and Temporal Mixture of Experts with Sequential Attention (TMESA)—to generate reliable real-time Tm estimates and enhance the accuracy of operational GNSS-PWV retrievals over China. A traditional linear model is adopted as the baseline to evaluate the performance improvements of the proposed models. The models are trained and tested using 10-year (2014–2023) hourly ERA5-derived Tm products and in-situ surface pressure, temperature, and relative humidity from 2377 meteorological stations, with Tm diurnal variations, station coordinates, and day of year integrated as auxiliary predictive features. Validation is conducted using 2024 ERA5 reanalysis data and radiosonde profiles from 120 stations across China. Results show that the RF model yields a bias (RMSE) of −0.11 K (2.67 K) against ERA5 and −0.21 K (2.67 K) against radiosonde data, while the TMESA model achieves superior performance with bias (RMSE) of −0.02 K (2.34 K) and 0.09 K (2.46 K), respectively, whose performance levels comparable to state-of-the-art studies. Compared with the traditional linear model, the RF model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the TMESA model achieves reductions of 40% and 33%, respectively. These findings confirm that the proposed ML models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Future work will focus on the operational application of these models for near-real-time GNSS-PWV estimation. Full article

Laser Powder Bed Fusion (LPBF) is a complex manufacturing process that depends on precise control of printing parameters and melt pool geometry, which directly influence defect formation and final part quality. This study evaluated the reliability of a simplified thermal conduction-based melt pool model by combining post-process metallographic analysis with in situ dual-wavelength infrared thermal imaging. Experimental data were obtained through single-track printing on 316L, IN625, and CoCr alloys across a wide range of parameters. The simulated melt pool length showed strong agreement with thermal camera measurements (R²_adj > 0.78), while the width showed moderate but consistent correlation (R²_adj > 0.52). For melt pool depth, the model systematically deviated due to its inability to capture keyhole melting, although a strong linear correlation was still observed (R²_adj > 0.86). Cross-validation between metallographic measurements and thermal imaging revealed only a 6–9% discrepancy, confirming the reliability of both methods and the potential of dual-wavelength cameras for industrial process monitoring. Overall, the model proves to be a reliable tool for predicting melt pool surface geometry specifically within the conduction melting regime, while its predictive capability degrades significantly in the keyhole regime, where simulated peak temperatures reach up to 7000 °C and melt pool depth errors escalate due to the disregard of recoil pressure, liquid and vapor dynamics. Full article

This review presents a process-based decision-making framework for chlorinated vapor intrusion (CVI) mitigation. CVI mitigation refers to the set of engineered strategies aimed at interrupting, attenuating or transforming vapor fluxes before they reach indoor environments. Existing literature and technical guidelines typically classify mitigation strategies according to technological configuration (active versus passive), rather than physical and chemical processes governing vapor transport and attenuation, which may lead to suboptimal design choices and reduced system resilience. To address this limitation, this framework proposes a process-based classification of CVI mitigation strategies based on the dominant mechanisms controlling vapor migration in subsurface. Five mechanistic categories are identified: driving-force control through pressure manipulation, dilution via air exchange, diffusive flux control through physical barriers, density-driven attenuation in permeable sub-slab layers, and in situ transformation based on sorption or degradation. By explicitly linking mitigation technologies to transport and transformation processes, the proposed framework provides a structured basis for mechanism-oriented selection, integrating performance, longevity, climate resilience, and lifecycle energy demand. In addition to established mitigation approaches, such as sub-slab depressurization, this work highlights emerging passive strategies, including high permeable granular layers and horizontal reactive or adsorbing barriers, as potential low-energy alternatives for durable management. Overall, the proposed framework supports site-specific, sustainability-oriented decision-making on CVI mitigation. Full article

Biostimulants from Ascophyllum nodosum (L.) are effective as regulators of molecular, physiological and biochemical processes in plants. Two independent experiments were conducted using foliar application in Rosa × hybrida variety White O’Hara of two A. nodosum-based biostimulant formulations (B1: A. nodosum (10% w/v), N, P₂O₅, K, Ca, Mg, oxidizable total organic carbon (3% w/v), minor elements, and free amino acids (3.9% w/v); B2: A. nodosum (11% w/v), oxidizable total organic carbon (6.8% w/v) N (37.2% w/v), and P₂O₅ (50% w/v)). Each experiment was conducted in a Randomized Complete Block Design (RCBD) with a factorial arrangement including four treatments (0; 0.5; 1.0; and 1.5 mL L⁻¹), which were evaluated over two production cycles. Foliar chlorophyll (μmol m⁻²), stomatal conductance (mmol m⁻² s⁻¹), and leaf vapor pressure deficit were measured every two weeks, and productivity was evaluated at the end of the cycle. Statistical differences were detected in chlorophyll content for the application of B1 and B2 over two production cycles with increases of around 16–17% in chlorophyll compared to the control. Significant differences in stomatal conductance were detected during weeks 20 and 22 for all doses. The control treatment consistently exhibited lower means for the leaf vapor pressure deficit compared to B1 and B2. Biostimulants improved photosynthetic activity and carbon assimilation and also delayed leaf senescence. B1 at 1 mL L⁻¹ reduced unproductive stems from 54% to 38% compared to the control. Biostimulant treatments enhanced physiological tolerance to temperature extremes (2.2–32.6 °C). Based on the results, 1.5 mL L⁻¹ of the B1 biostimulant and 1 mL L⁻¹ of the B2 are recommended; these findings offer key insights for optimizing rose cultivation and prove that intensive floriculture can be both productive and sustainable. Full article

In the bourbon industry, rickhouses store bourbon barrels undergoing the maturation process. Ambient conditions—including temperature, relative humidity, and overall air composition—play a critical role in the maturation process of bourbon within rickhouses. The presence of ethyl alcohol vapors is a byproduct of the aging process and has been a long-standing issue within the industry. Exposure to ethanol vapor can hasten the corrosion of barrel hoops, potentially compromise the integrity of the barrels and lead to product loss. Newly constructed rick-houses have been designed to mitigate the vapors with natural ventilation from windows and air vents. This study shows that natural ventilation does not really allow air to move through the stacks, even in an empty rickhouse. The evaluation was performed using differential pressure measurements and smoke tracing to characterize extremely low-energy airflow. Differential pressure measurements and smoke tracing conducted on the first floor and crawl space of a newly constructed empty rickhouse indicated that while air enters the warehouse through windows and vents, it does not effectively penetrate the interior rick structures. Airflow is largely confined to the crawl space and walkways, with limited movement into the central rick areas, indicating that natural ventilation alone may be insufficient for comprehensive air circulation. The findings provide important insights into airflow behavior and its implications for the spirits industry, while contributing to a growing body of evidence suggesting that natural ventilation alone may be insufficient to adequately mitigate a known de-passivating agent, ethyl alcohol vapor, accumulation in current rickhouse designs. The results align with the United Nations Sustainable Development Goals of “Sustainable Cities and Communities” (SDG 11) and “Responsible Consumption and Production” (SDG 12). Improved understanding of airflow characteristics may support the development of better-ventilated rickhouses, enhancing sustainable production practices and reducing the impact of material and product losses on surrounding communities. Full article

It has been observed in prior research that high thermal impact—resulting from a large temperature difference between hot water vapor and cold liquid water—can enhance the thermal performance of cavitation-induced low-energy nuclear reactions (LENRs) in water, with an estimated increase in the coefficient of performance (COP) of approximately 50% for every 100 °C temperature rise. The temperature of the hot water vapor is primarily determined by the boiler output, which typically represents the highest temperature source and plays a dominant role in reactor performance. In this study, a flow oscillator was designed as an thermal conditioning component for these potential LENR reactor systems using linear flow network analysis (LFNA) to generate flow resonance that elevates the hot vapor temperature, thereby increasing thermal impact and improving LENR performance. LFNA is based on the linearization of the fluid flow equations governing mass and momentum transport and utilizes a fluid-electric circuit analogy. For a fluid flow system, various components can be modeled using analogs of electrical resistance, capacitance, and inductance (R, C, and L), allowing the system behavior to be analyzed similarly to an RLC circuit. Through this analogy, flow resonance phenomena can be predicted, potentially enabling the generation of high-temperature and high-pressure responses that are beneficial to LENR processes. The analytical model was experimentally validated and subsequently applied in the LENR reactor design. The analytical result shows that an output temperature difference exceeding 350 °C can be achieved using a 0.5 m pulse tube at a 46 Hz triggering frequency with 20 kPa perturbation, which indicates a potential COP enhancement of 175% based on prior studies. The result provides a potential mechanism to significantly enhance the thermal impact conditions and promote LENR performance in water-based reactor systems. Full article

Revegetation serves as a critical ecological safeguard, while these interventions have added complexity to the evapotranspiration processes and water balance. Dalinor Lake basin (DLB), located in the southeast of Inner Mongolia Plateau, serves as a vital habitat for migratory birds and plays an important role in the ecological security of northern China. To enhance biodiversity, numerous ecological restoration projects have been carried out in this area in recent years. Dalinor Lake, a large inland lake within the basin, has experienced persistent shrinkage. Although existing studies have explored its driving factors, the potential influence of revegetation activities on lake shrinkage remains unclear. In this study, we used remote sensing imagery, combined with supervised classification and visual interpretation methods, to extract changes in the surface areas of lakes within the DLB (i.e., Dalinor Lake and Ganggeng Lake), and analyzed the effects of total terrestrial evapotranspiration (ET_t), precipitation (PPT), runoff, soil moisture content, and the vapor pressure deficit on these changes. Results showed that the Dalinor Lake’s area decreased by 18.68% from 2000 to 2020, and was mainly influenced by ET_t, with the Normalized Difference Vegetation Index (NDVI) contributing the most to ET_t (54.02%). In contrast, Ganggeng Lake expanded by 5.68% and was strongly driven by PPT. Compared with Ganggeng Lake, there have been more revegetation activities around Dalinor Lake, resulting in significant increases in NDVI and ET_t, together with widespread declines in soil moisture in its surrounding areas, suggesting that revegetation exerted non-negligible water pressure on Dalinor Lake. These findings can provide valuable information for policymakers to balance large-scale ecological restoration with sustainable water management in semi-arid regions. Full article

Research and development of novel CO₂-selective membranes, called molecular gate membranes (MGMs), has been conducted. Unlike conventional CO₂-selective membranes, MGMs show exceptionally high CO₂ separation over H₂. The membranes and the membrane modules were developed for CO₂ separation at low energy consumption and low cost in pre-combustion processes such as integrated gasification combined cycle (IGCC) and hydrogen production. To date, two candidate membrane materials—poly(ethylene glycol) (PEG)-based and poly(vinyl alcohol) (PVA)-based membranes—have been used. As for PEG-based membrane materials, the effect of operating conditions, such as relative humidity in feed gas and sweep gas and operating pressure, on CO₂ separation performance were investigated. Both CO₂ permeance and selectivity increased with increasing relative humidity on both the feed and permeate sides. The CO₂ permeance increased from the 10⁻¹² to the 10⁻¹¹ order, while the selectivity increased from 2.8 to 25. In addition, it was found that the water vapor permeates from the high to the low relative humidity side with a permeance typically on the order of 10⁻⁸ m³(STP)m⁻²·s⁻¹·Pa⁻¹, regardless of the total pressure difference between the feed side and the permeate side. This finding is important in the design of membrane systems. However, we found that PVA-based membranes exhibited superior thin-film coating ability and higher separation performance compared with PEG-based membranes. As for PVA-based materials, membranes that showed high CO₂ separation performance under high-pressure conditions of 2.4 MPa (the supposed pressure in the IGCC process) were successfully prepared. In addition, the technology to prepare MGMs with a large membrane area was developed by a continuous membrane-forming method, and the membrane elements (diameter: 10–20 cm; length: 20–60 cm) were also fabricated. Pre-combustion CO₂ capture tests of the membrane elements were conducted using coal-derived gasification gas, and it was confirmed that the membrane elements were durable against the real gas, which contained components such as H₂S (on the order of 100 ppm) and CO (32.4%). Full article

Conventional liquid springs enable storage of energy in the form of interfacial tension at forcibly wetted lyophobic surfaces. The pressure–volume work performed to compress the liquid into a poorly wettable porous medium is recovered during spontaneous expulsion when pressure falls below the capillary pressure characteristic of a given system. Our study explores generalizations to easily wettable materials where liquid infiltration is opposed solely by steric hindrance exerted on liquid molecules in micro-sized pores. The concept is exemplified in molecular simulations of prototypical model systems with methanol intruding narrow slits between hydrocarbon or graphene surfaces. While these materials show significant wetting propensities at macroscopic interfaces with liquid methanol, substantial compression is required to wet molecular-sized pores barely accommodating a monolayer of liquid molecules. The observed O(10³) bar intrusion pressures secure stored energy densities competitive with supercapacitors and amenable to improvement. Wall–liquid attraction and small pore diameters lead to intrusion–expulsion pathways along cooperative-adsorption isotherms. The process avoids abrupt liquid/vapor transitions and associated nucleation barriers, responsible for cycle hysteresis in experiments with water in hydrophobic capillaries. Using open ensemble (Grand Canonical) Monte Carlo sampling, we identify the range of porosities supporting reversible energy storage/recovery operation in lyophilic media; the results can assist with the design of molecular spring devices with competitive storage and power capacities in pragmatic contexts. Full article

This work presents the thermodynamic design and performance assessment of an integrated process line for the separation, liquefaction, storage, and valorization of carbon dioxide (CO₂) and nitrogen (N₂) from flue gas streams. The proposed system aims to combine carbon capture with cryogenic energy storage by exploiting the thermophysical properties of the main flue gas constituents. A representative flue gas derived from complete methane combustion (9.5% CO₂, 71.5% N₂, and 19% H₂O by volume) is considered as the feed stream. The process is developed and simulated in DWSIM v9.0.5, adopting a steady-state mass and energy balance framework coupled with rigorous thermodynamic modeling of phase equilibria and unit operations. The plant configuration is based on sequential cooling, compression, and expansion stages, enabling the selective condensation of H₂O, CO₂, and N₂ at different temperature levels. The system integrates heat exchangers, compressors, pumps, turboexpanders, phase separators, and cryogenic storage tanks, while a portion of the liquefied CO₂ is reused as an energy carrier through vaporization and expansion in a dedicated turbine. The results demonstrate that the process achieves a CO₂ capture ratio of 81.7%, with a specific electric consumption (SEC) of 10.44 kWh/kg_CO2 and 1.71 kWh/kg_N2. The overall net electric demand is 1.29 kWh/kg of treated flue gas, while the round-trip efficiency (η_RT,CO2) is 18.6%. A significant amount of energy can further be recovered from the “waste” exhaust water stream (12.94 kg_L-H2O/kg_flue-gas, at 91 °C and 1.2 bar) up to 800 Wh/kg_flue-gas, improving the performance of the entire process (SEC_CO2: 3.86 kWh/kg_CO2, η_RT,CO2: 69.8%). The study confirms the thermodynamic feasibility of the proposed configuration and identifies nitrogen liquefaction as the dominant energy-intensive step. Future optimization efforts should therefore focus on reducing exergy destruction in the deep cryogenic section through improved heat integration, enhanced cold-energy recovery, optimized compression–expansion staging, and reduced pressure losses. Full article

Optically pumped magnetometers (OPM) are core quantum payloads for geomagnetic remote sensing. Among them, the spin-exchange relaxation-free (SERF) OPM with aT-level ultimate sensitivity stands as mainstream. While enlarging the alkali-metal vapor cell of the SERF OPM enhances sensitivity, it triggers complex atomic spin relaxation, notably intensified magnetic field gradient relaxation. To address the dilemma of atomic spin relaxation regulation and the engineering requirements of ultra-high-sensitivity SERF magnetometers, this paper constructs an analytical model of the total relaxation rate that comprehensively considers wall-collision relaxation, spin-destruction collision relaxation, and longitudinal/transverse magnetic field gradient relaxation, etc. The analytical relationship between buffer gas pressure and total relaxation rate for a commonly used spherical vapor cell is derived, revealing the intrinsic correlation among cell size, atomic spin relaxation, and optimal pressure. Based on the theoretical model, the filling parameters of the vapor cell are optimized, and experimental measurements are carried out. The theoretical relaxation results are highly consistent with the experimental ones, realizing the precise optimization of buffer gas pressure. The optimization method proposed in this paper provides a theoretical basis and parameter guidance for the engineering preparation of alkali-metal vapor cells for high-sensitivity SERF magnetometers in remote sensing applications. Full article

Resource-constrained edge processors deployed on unmanned aerial vehicles and wearable platforms require compact, drift-robust gas classification models for a range of environmental and security monitoring applications, including CBRN-motivated scenarios. Existing approaches rely on server-grade architectures incompatible with edge-board-scale deployment, or on classifiers that chemically degrade severely under long-term sensor drift. Each UCI gas class was mapped to a CBRN behavioral category based on physicochemical analogy (molecular functional group, vapor pressure, and metal-oxide semiconductor (MOS) cross-sensitivity pattern), following established precedent. Analyzed were Ammonia (NH₃), Acetaldehyde (CH₃CHO), Acetone ((CH₃)₂CO), Ethylene (C₂H₄), Ethanol (C₂H₅OH), Toluene (C₆H₅CH₃). We propose herein an end-to-end pipeline integrating a novel 1-D convolutional neural network with depth-wise separable convolutions (LiteSensor-Net), INT8 post-training quantization, structured magnitude pruning, and a knowledge-distillation domain-adaptation module (KD–DM) for sensor drift compensation. Using the UCI Gas Sensor Array Drift Dataset (13,910 measurements; 16 metal-oxide sensors; six analyte gases; a 36-month work span). LiteSensor-Net achieved accuracy = 92.63 ± 2.02%, macro-F1 = 0.898, model size = 5.99 kB INT8 pruned, inference latency = 6.3 ms, RAM footprint = 31.7 kB, and energy per inference = 0.04 mJ (all metrics on Raspberry Pi 4B, ARM Cortex-A72). Under chronological forward-chaining evaluation, KD–DM–20 achieved 47.91 ± 18.79% mean accuracy over Batches 2–10, representing a +9.25 pp improvement over uncompensated NC (38.66%). A six-metric benchmark framework—accuracy, macro-F1, model size, inference latency, RAM footprint, and energy per inference—is introduced to standardize edge-AI gas classifier evaluation. The proposed pipeline provides an open-source, deployable foundation for edge-class gas classification systems, with CBRN detection as a motivating application. Full operational validation on certified chemical simulants remains as future work. Full article

This work presents advanced binder-free self-assembling Zn nanowire anodes synthesized by an easy-to-handle one-step low-pressure physical vapor deposition method. The morphology and structure of zinc nanowire networks are controlled and altered by the substrate temperature during deposition. Electrochemical performance of two types of Zn nanowire network samples of different morphology is studied in alkaline and mildly acidic aqueous electrolytes using cyclic voltammetry and electrochemical impedance spectroscopy techniques and compared to that of Zn foil electrodes. It is found that the morphology and structure of the Zn nanowire electrodes are directly related to their electrochemical performance and can be tuned for the type and concentration of the electrolyte to reach optimal electrochemical performance. The resulting binder-free self-assembled Zn nanowire anodes significantly outperform traditional Zn-based electrodes in both mild acidic and alkaline electrolytes, showing an areal capacitance of ~3.3 F/cm² and 3.5 F/cm² for acidic and alkaline electrolytes, respectively, and stability up to 1000 h of cycling in mild acidic electrolytes. These findings provide a pathway to fabricate and optimize binder-free zinc anodes for a variety of efficient and long-lasting aqueous zinc-based batteries and supercapacitors. Full article