Search Results (98)

Although technologies used in non-fossil methane and fossil resources to produce blue hydrogen are relatively mature, a system-integrated approach to reference system (RS)-based purification of H₂, CO₂ capture and storage, and UHS is relatively unexplored and requires research to fill gaps in the literature regarding balanced permutations and geological viability for net-zero requirements. This research proposes a system-integrated process for H₂ production through a PSA-based purification technique coupled with amine-based CO₂ capture and underground hydrogen storage (UHS). The intellectual novelty of the research is its first quantitative treatment of synergistic effects such as heat recovery and pressure-matching across units. Additionally, a site separation technique is applied, where H₂ and CO₂ reservoirs are selected based on the permeability of rock formations and fluids. On a research methodology front, a base case of a steam methane reforming process with the production of 99.99% pure H₂ at a production rate of 5932 kg/h is modeled and simulated using Aspen Plus™ to create a balanced permutation of mass and energy across units. As per the CO₂ capture requirements of this research, a capture of 90% of CO₂ is accomplished from the production of 755 t/d CO₂ within the model. The compressed CO₂ is permanently stored at specifically identified rock strata separated from storage reservoirs of H₂ to avoid empirically identified hazards of rock–fluid interaction at high temperatures and pressures. The lean amine cooling of CO₂ to 60 °C and elimination of tail-gas recompression simultaneously provides 5.4 MW_th of recovered heat. The integrated design achieves a net primary energy penalty of 18% of hydrogen’s LHV, down from ~25% in a standalone configuration. This corresponds to an energy saving of 8–12 MW, or approximately 15–18% of the primary energy demand. The research computes a production cost of H₂ of 0.98 USD per kg of H₂ within a production atmosphere of a commercialized WGS and non-fossil methane-based production of H₂. Additionally, a sensitivity analysis of ±23% of the energy requirements of the reference system shows no marked sensitivity within a production atmosphere of a commercially available WGS process. Full article

The use of e-fuels, such as methanol (MeOH), is considered an alternative for the reduction of carbon emissions. MeOH can be produced from captured CO₂ and green H₂, with the exothermic (equilibrium-limited) reaction favoured at low temperatures and high pressures. However, CO₂ is a very stable molecule and requires high temperature (>200 °C) to overcome the slow activation kinetics. In this study, MeOH was synthesized from CO₂ and H₂ in a packed-bed membrane reactor (PBMR) using a commercial Cu/ZnO/Al₂O₃ catalyst and a tubular-supported, water-selective composite alumina–carbon molecular sieve membrane (Al-CMSM) immersed in the catalytic bed. A mixture of H₂/CO₂ (3/1) was fed into both sides of the membrane to increase the driving force of the gases produced by the reaction. The effect of the temperature of reaction (200, 220, and 240 °C), pressure difference (0 and 3 bar), and the sweep gas/reacting gas ratio (SW = 1, 3, 5) in the CO₂ conversion and products yield was studied. For comparison, the reactions were also carried out in a packed-bed reactor (PBR) configuration where the tubular membrane was replaced by a metallic tube of the same size. CO₂ conversion and MeOH yield are much higher in PBMR than in PBR configuration, showing the benefit of using the water-selective membrane. In PBMR, MeOH yield increases with SW and slightly decreases with the temperature, overcoming the limitation imposed by the thermodynamics. Full article

CO₂ methanation offers a promising technology to convert CO₂ into methane, a valuable fuel that can be integrated into existing gas infrastructure. However, developing cost-effective, highly active, and stable catalysts remains a key challenge. In this paper, a series of La/NiAl-LDO catalysts were synthesized via a coprecipitation–impregnation method for catalytic CO₂ hydrogenation. Among the prepared catalysts, 6La/NiAl-LDO exhibited the highest CO₂ conversion (85.6%) with nearly 100% CH₄ selectivity at 300 °C and 2 MPa. The catalyst also demonstrated excellent stability over a 100 h durability test. Moreover, the kinetics of CO₂ hydrogenation over a 6La/NiAl-LDO catalyst were studied in a fixed-bed reactor at a catalyst particle size of 20–40 mesh, space velocity of 8000 mL/(g·h)), and temperatures ranging from 260 to 300 °C. The overall positive reaction followed approximately first-order kinetics, with an apparent activation energy of 89.4 kJ/mol. This work contributes to broader efforts in CO₂ capture and conversion to synthetic natural gas. Full article

Background: The Fc region of immunoglobulin G (IgG) is a key target in therapeutic and analytical applications, such as antibody purification and site-specific bioconjugation. Although Protein A exhibits strong Fc-binding affinity, its large molecular weight and limited chemical flexibility pose challenges for use in compact or chemically defined systems. To address these limitations, we designed two α-helical peptides, SpA h1 and SpA h2, based on the Fc-binding helices of the Z34C domain from Staphylococcus aureus Protein A. Method: To enhance the structural stability and Fc-binding capability of these peptides, a lactam-based stapling strategy was employed by introducing lysine and glutamic acid residues at positions i and i + 4. Result: The resulting stapled peptides, (s)SpA h1 and (s)SpA h2, exhibited significantly improved α-helical content and IgG-binding performance, as demonstrated by circular dichroism (CD) spectroscopy and fluorescence-based IgG capture assays. Surface plasmon resonance (SPR) analysis confirmed specific, concentration-dependent interactions with the Fc region of human IgG, with (s)SpA h1 consistently showing the binding affinity and stability. Proteolytic resistance assays using α-chymotrypsin revealed that (s)SpA h1 maintained its structural integrity over time, exhibiting markedly enhanced resistance to enzymatic degradation compared to its linear counterpart. Furthermore, (s)SpA h1 exhibited strong Fc selectivity with minimal Fab affinity, confirming its suitability as a compact and Fc-specific binding ligand. Conclusions: These results confirm the successful design and development of structurally reinforced Fc-binding peptides that overcome the inherent limitations of short linear sequences through both high-affinity sequence optimization and lactam-based stapling. Among them, (s)SpA h1 demonstrates the most promising characteristics as a compact yet stable Fc-binding ligand, suitable for applications such as antibody purification and site-specific bioconjugation. Full article

Background: In Siberian folk medicine, Sagan-Dalya (Rhododendron adamsii Rehder) of the Ericaceae family is used as a tonic and restorative in the form of infusions and decoctions. Pharmacological studies have shown that alcoholic extracts of this plant enhance performance and have anti-inflammatory and immunomodulatory effects. Rhododendron adamsii shoots accumulate essential oil (up to 1.6%), flavonoids (1.8–3.0%), tannins (up to 6.9%), phenolic carbolic acids, β-sitosterin, oleanolic and ursolic acids, simple phenolic compounds, and coumarins. Methods: Supercritical carbon dioxide extraction (SC-CO₂) is the most preferred environmentally friendly and selective method for extracting these natural compounds from the plant matrix of Rh. adamsii due to their high thermolability. Tandem mass spectrometry was applied to detect chemical compounds. Mass-spectrometry (MS) analysis was performed on an ion trap equipped with an ESI source in negative and positive ion modes. The capture rate was one spectrum/s for MS and two spectrum/s for MS/MS. All experiments were repeated three times. A four-stage ion separation mode (MS/MS mode) was implemented. Results: The operative parameters and working conditions have been optimized by different pressure (100–400 bars) and temperature (31–70 °C) regimes, and CO₂ flow rate (10–25 mL/min) with 1 C₂H₅OH as a co-solvent. The extraction time varied from 60 to 90 min. The maximum global yield of biologically active substances (BAS) from R. adamsii leaves and stems was observed under the following extraction conditions: Pressure: 350 bar, extraction temperature: 65 °C, extraction time: 1 h; the global yield of BAS was 8.5 mg/g of plant sample; the share of the co-solvent (C₂H₅OH) was 2%. In total, forty-nine different BAS were identified in the Rh. adamsii SC-CO₂ extracts. Conclusions: The obtained results may shed new light on the scientific basis for the traditional medicinal use of Rh. adamsii leaf and stem extracts. The pharmacological contribution of the identified phytocannabinoids requires further detailed study. It is hypothesized that the excellent transdermal permeability of supercritical extracts may open new therapeutic approaches using transdermal formulations based on SC-CO₂ extracts of Rh. adamsii. Full article

The industrial application of CO₂ methanation in Power-to-X (P2X) systems requires the development of highly active catalysts capable of operating at milder temperatures to ensure energy efficiency, while exhibiting high activity, stability and selectivity. This study reports the synthesis and optimization of Ni-based catalysts on Al₂O₃ supports, guided by a Design of Experiments (DoE, 2⁴ factorial design) approach. Initial optimization afforded a robust catalyst achieving 80% CO₂ conversion and >99% CH₄ selectivity at 325 °C. Remarkably, the incorporation of CeO₂ traces to the Ni-based catalyst substantially boosted catalytic activity, enabling higher conversions at temperatures up to 75 °C lower than the unpromoted catalyst. This improvement is attributed to Ni–CeO_x synergy, which facilitates CO₂ activation and Ni reducibility. Both formulations exhibited exceptional long-term stability over 100 h. Furthermore, process intensification via the Sorption-Enhanced Reaction Process (SERP) with the Ni-based catalyst demonstrated even superior efficiency, rapidly increasing CO₂ conversion beyond 95% with the same selectivity range. Our findings establish a clear and consistent pathway for industrial CO₂ valorization through next-generation P2X technology for high-purity synthetic natural gas (SNG) production. This process offers an efficient and sustainable route toward industrial defossilization by converting captured CO₂ and green H₂ into SNG that is readily usable within the existing energy infrastructure. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

This study evaluates the impact of mineralogy, elemental composition, and reaction pathways on hydrogen (H₂) generation in seven ultramafic and mafic (basaltic) rocks. Experiments were conducted under typical low-temperature hydrothermal conditions (150 °C) and captured early and evolving stages of fluid–rock interaction. Pre- and post-interactions, the solid phase was analyzed using X-ray Diffraction (XRD) and X-ray Photoelectron Spectroscopy (XPS), while Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was used to determine the composition of the aqueous fluids. Results show that not all geologic H₂-generating reactions involving ultramafic and mafic rocks result in the formation of serpentine, brucite, or magnetite. Our observations suggest that while mineral transformation is significant and may be the predominant mechanism, there is also the contribution of surface-mediated electron transfer and redox cycling processes. The outcome suggests continuous H₂ production beyond mineral phase changes, indicating active reaction pathways. Particularly, in addition to transition metal sites, some ultramafic rock minerals may promote redox reactions, thereby facilitating ongoing H₂ production beyond their direct hydration. Fluid–rock interactions also regenerate reactive surfaces, such as clinochlore, zeolite, and augite, enabling sustained H₂ production, even without serpentine formation. Variation in reaction rates depends on mineralogy and reaction kinetics rather than being solely controlled by Fe oxidation states. These findings suggest that ultramafic and mafic rocks may serve as dynamic, self-sustaining systems for generating H₂. The potential involvement of transition metal sites (e.g., Ni, Mo, Mn, Cr, Cu) within the rock matrix may accelerate H₂ production, requiring further investigation. This perspective shifts the focus from serpentine formation as the primary driver of H₂ production to a more complex mechanism where mineral surfaces play a significant role. Understanding these processes will be valuable for refining experimental approaches, improving kinetic models of H₂ generation, and informing the site selection and design of engineered H₂ generation systems in ultramafic and mafic formations. Full article

This study proposes a novel causality-structured matrix long short-term memory (C-mLSTM) model for significant wave height (SWH) forecasting. The framework incorporates a two-stage causal feature selection methodology using cointegration testing and Granger causality testing to identify long-term stable causal relationships among variables. These relationships are embedded within the C-mLSTM architecture, enabling the model to effectively capture both temporal dependencies and causal information within the data. Furthermore, the model integrates Bayesian optimization (BO) and twin delayed deep deterministic policy gradient (TD3) algorithms for synergistic optimization. This combined TD3-BO approach achieves an 11.11% improvement in the mean absolute percentage error (MAPE) on average compared to the base model without optimization. For 1–24 h SWH forecasts, the proposed TD3-BO-C-mLSTM outperforms the benchmark models TD3-BO-LSTM and TD3-BO-mLSTM in prediction accuracy. Finally, a Shapley additive explanations (SHAP) analysis was conducted on the input features of the BO-C-mLSTM model, which reveals interpretability patterns consistent with the two-stage causal feature selection methodology. This research demonstrates that integrating causal modeling with optimization strategies significantly enhances time-series forecasting performance. Full article

With the increase in contamination by microbial agents (bacteria, viruses, etc.) in the fields of agri-food, healthcare, and environment, it is necessary to detect and quantify these biological elements present in complex fluids in a short time with high selectivity, high sensitivity, and, if possible, moderate cost. Acoustic wave biosensors, based on immuno-detection, appear to meet a certain number of these criteria. In this context, we are developing a generic antibody-based biointerface that can detect a wide range of pathogenic bacterial agents using a specific bioreceptor. Based on the silane–oxide chemistry, the process is transferable to any kind of surface that can be either oxidized in surface or activated with O₂-plasma, for instance. For this proof of concept, we have chosen to develop our biointerface on titanium and lithium niobate surfaces. The development of the biointerface consists of grafting antibodies via a self-assembled monolayer (SAM) composed of an aminopropyltriethoxysilane (APTES) and a linker (phenylene diisothiocyanate, PDITC). Two functionalization routes were tested for grafting APTES: in anhydrous toluene followed by a heating step at 110 °C or in chloroform at room temperature. The results obtained on titanium show comparable grafting efficiency between these two routes, allowing us to consider the transposition of the route at room temperature on lithium niobate. The latest route was chosen for fragile materials that do not require the heating steps necessary when using toluene for grafting aminopropyltriethoxysilane. Different surface characterization techniques were used, such as IR spectroscopy (FTIR-ATR), X-ray photoelectron spectroscopy (XPS), and contact angle (WCA), to verify the successful grafting of each layer. Biodetection experiments in static conditions were also carried out to demonstrate the specificity of pathogenic detection, testing an ideal medium with solely bacteria, with no other food sampling nutrients. This paper demonstrates the successful elaboration of a biointerface using APTES as the first anchoring layer, with chloroform as a mild solvent. The process is easily transferable to any kind of fragile surface. Moreover, following anti-L. monocytogenes antibodies, our biointerface shows a specificity of capture in static mode (at a concentration of 10⁷ CFU/mL for an incubation time of 4 h at 37 °C) of up to 98% compared to a species negative control (E. coli) and up to 85% in terms of strain specificity (L. innocua). Full article

This study proposes a novel non-destructive approach to assessing and predicting the quality of stored date fruits using a composite quality index (Qi) modeled via visible–near-infrared (VIS–NIR) spectroscopy and artificial neural networks (ANNs). Two leading cultivars, Sukkary and Khlass, were stored for 12 months using three temperature regimes (25 °C, 5 °C, and −18 °C) and five types of packaging. The samples were grouped into six moisture content categories (4.36–36.70% d.b.), and key physicochemical traits, namely moisture, pH, hardness, total soluble solids (TSSs), density, color, and microbial load, were used to construct a normalized, dimensionless Qi. Spectral data (410–990 nm) were preprocessed using second-derivative transformation and modeled using partial least squares regression (PLSR) and the ANNs. The ANNs outperformed PLSR, achieving the correlation coefficient (R²) values of up to 0.944 (Sukkary) and 0.927 (Khlass), with corresponding root mean square error of prediction (RMSEP) values of 0.042 and 0.049, and the relative error of prediction (REP < 5%). The best quality retention was observed in the dates stored at −18 °C in pressed semi-rigid plastic containers (PSSPCs), with minimal microbial growth and superior sensory scores. The second-order Qi model showed a significantly better fit (p < 0.05, AIC-reduced) over that of linear alternatives, capturing the nonlinear degradation patterns during storage. The proposed system enables real-time, non-invasive quality monitoring and could support automated decision-making in postharvest management, packaging selection, and shelf-life prediction. Full article

The CO₂ capture from flue gas using biomass-derived porous carbons presents an environmentally friendly and sustainable strategy for mitigating carbon emissions. However, the conventional fabrication of porous carbons often relies on highly corrosive activating agents like KOH and ZnCl₂, posing environmental and safety concerns. To address this challenge, in the present work sodium metaborate tetrahydrate (NaBO₂·4H₂O) has been utilized as an alternative, eco-friendly activating agent for the first time. Moreover, a water chestnut shell (WCS) is used as a sustainable precursor for boron-doped porous carbons with varied microporosity and boron concentration. It was found out that pyrolysis temperature significantly determines the textural features, elemental composition, and CO₂ adsorption capacity. With a narrow micropore volume of 0.27 cm³/g and a boron concentration of 0.79 at.% the representative adsorbent presents the maximum CO₂ adsorption (2.51 mmol/g at 25 °C, 1 bar) and a CO₂/N₂ selectivity of 18 in a 10:90 (v/v) ratio. Last but not least, the as-prepared B-doped carbon adsorbent possesses a remarkable cyclic stability over five cycles, fast kinetics (95% equilibrium in 6.5 min), a modest isosteric heat of adsorption (22–39 kJ/mol), and a dynamic capacity of 0.80 mmol/g under simulated flue gas conditions. This study serves as a valuable reference for the fabrication of B-doped carbons using an environmentally benign activating agent for CO₂ adsorption application. Full article

The core challenge of integrated carbon capture and utilization (ICCU) technology lies in developing electrolytes that combine efficient carbon dioxide (CO₂) capture with electrocatalytic conversion capabilities. This review analyzes the structure–performance relationship between electrolyte properties and CO₂ electrochemical reduction (eCO₂RR), revealing the key regulatory mechanisms. Research shows that the performance of bicarbonate electrolytes heavily depends on the cation type, where Cs⁺ can achieve over 90% CO selectivity by suppressing the hydrogen evolution reaction (HER) and stabilizing reaction intermediates, though its strong corrosiveness limits practical applications. Although amine absorbents excel in carbon capture (efficiency > 90%), they tend to undergo competitive adsorption during electrocatalysis, making formic acid the primary product (FE = 15%); modifying electrodes with ionomers can enhance their activity by 1.15 times. Ionic liquids (ILs) demonstrate unique advantages due to their tunability: imidazolium-based ILs improve formate selectivity to 85% via carboxylate intermediate formation, while amino-functionalized task-specific ILs (TSILs) achieve a 1:1 stoichiometric CO₂ absorption ratio. Recent breakthroughs reveal that ternary IL hybrid electrolytes can achieve nearly 100% CO Faradaic efficiency (FE) through microenvironment modulation, while L-histidine additives boost CH₄ selectivity by 23% via interface modification. Notably, constructing a “bulk acidic–interfacial neutral” pH gradient system addresses carbonate deposition issues in traditional alkaline conditions, increasing C₂₊ product efficiency to 50%. Studies also highlight that cation–anion synergy (e.g., K⁺/I⁻) significantly enhances C-C coupling through electrostatic interactions, achieving 97% C₂₊ selectivity on Ag electrodes. These findings provide new insights for ICCU electrolyte design, with future research focusing on machine learning-assisted material optimization and reactor engineering to advance industrial applications. Full article

Algal blooms pose significant risks to public health and aquatic ecosystems, highlighting the need for real-time water quality monitoring. Traditional manual methods are often limited by delays in data collection, which can hinder timely response and effective management. This study proposes a solution by integrating automated monitoring systems (AMSs) with advanced machine learning (ML) techniques to predict chlorophyll-a ($Chl-a$) concentrations. Utilizing low-cost and readily available input variables, we developed energy-efficient ML algorithms optimized for deployment on buoys with a battery and hardware resources. The AMS employs preprocessing methods like the SMOTE and Random Forest (RF) for feature selection and ranking. Deep feature extraction is performed through a ResNet-18 model, while temporal dependencies are captured using a Long Short-Term Memory (LSTM) network. A Softmax output layer then predicts $Chl-a$ concentrations. An alert system is incorporated to warn when $Chl-a$ levels exceed 10 μg/L, signaling potential bloom conditions. The results show that this approach offers a rapid, cost-effective, and scalable solution for real-time water quality monitoring, enhancing manual sampling efforts and improving management of water bodies at risk. Full article

In recent years, frequent open biomass burning (OBB) activities such as agricultural residue burning and forest fires have led to severe air pollution and carbon emissions across South and Southeast Asia (SSEA). We selected this area as our study area and divided it into two sub-regions based on climate characteristics and geographical location: the South Asian Subcontinent (SEAS), which includes India, Laos, Thailand, Cambodia, etc., and Equatorial Asia (EQAS), which includes Indonesia, Malaysia, etc. However, existing methods—primarily emission inventories relying on burned area, fuel load, and emission factors—often lack accuracy and temporal resolution for capturing fire dynamics. Therefore, in this study, we employed high-resolution fire point data from China’s Feng Yun-4A (FY-4A) geostationary satellite and the Fire Radiative Power (FRP) method to construct a daily OBB emission inventory at a 5 km resolution in this region for 2020–2022. The results show that the average annual emissions of carbon (C), carbon dioxide (CO₂), carbon monoxide (CO), methane (CH₄), non-methane organic gases (NMOGs), hydrogen (H₂), nitrogen oxide (NO_X), sulfur dioxide (SO₂), fine particulate matter (PM_2.5), total particulate matter (TPM), total particulate carbon (TPC), organic carbon (OC), black carbon (BC), ammonia (NH₃), nitric oxide (NO), nitrogen dioxide (NO₂), non-methane hydrocarbons (NMHCs), and particulate matter ≤ 10 μm (PM₁₀) are 178.39, 598.10, 33.11, 1.44, 4.77, 0.81, 1.02, 0.28, 3.47, 5.58, 2.29, 2.34, 0.24, 0.58, 0.43, 0.99, 1.87, and 3.84 Tg/a, respectively. Taking C emission as an example, 90% of SSEA’s emissions come from SEAS, especially concentrated in Laos and western Thailand. Due to the La Niña climate anomaly in 2021, emissions surged, while EQAS showed continuous annual growth at 16.7%. Forest and woodland fires were the dominant sources, accounting for over 85% of total emissions. Compared with datasets such as the Global Fire Emissions Database (GFED) and the Global Fire Assimilation System (GFAS), FY-4A showed stronger sensitivity and regional adaptability, especially in SEAS. This work provides a robust dataset for carbon source identification, air quality modeling, and regional pollution control strategies. Full article