Experimental investigation of iron combustion in O2-containing mixtures of different diluent inert gases

Stage-resolved degradation-safety framework for NCM/graphite cells under low-temperature and high-C-rate cycling, linking Li plating, interfacial reconstruction and structural damage to reduced TR onset, increased combustible vent gases and progressively aggravated safety risk over the entire lifecycle. Degradation and safety are the fundamental imperatives of batteries, tightly intertwined and inseparable. Only by understanding the coupled mechanisms of degradation and thermal runaway (TR) can we gain control over battery safety across the entire lifecycle. In this work, we investigate commercial 20 Ah NCM/graphite pouch cells subjected to low-temperature and fast-charging coupled protocols, and track how their degradation from the beginning of life (BOL) to the end of life (EOL) reshapes TR behavior. By combining electrochemical diagnostics with multi-scale post-mortem characterization, adiabatic TR tests on full cells, electrode-level differential scanning calorimetry and post-TR gas chromatography, we build a stage-resolved framework that links interfacial composition, lithium plating and structural damage to changes in TR onset, heat release and gas composition. The results reveal a clear degradation critical point at around 90 % remaining capacity, at which the dominant degradation mode shifts from mild loss of lithium inventory to lithium-plating-driven accelerated aging with pronounced interface reconstruction, leading to reduced T1/T2, stronger low-temperature anode exotherms and a transition from CO 2 -dominated to H 2 - and hydrocarbon-rich vented gases. Notably, loss of active material already initiates in the BOL→CP stage (particle-scale defects/local continuity loss), although capacity fade remains predominantly governed by loss of lithium inventory. With further degradation towards EOL, superposed interfacial thickening, cathode cracking and loss of active material further narrow the safety margin and increase TR severity relative to the remaining energy. This work provides a concise mechanistic link between specific degradation signatures and TR metrics, offering practical guidance for defining aging-aware safety envelopes and designing low-temperature fast-charging/discharging strategies for energy storage systems.

Feasibility study of co-mixing biomass with additive waste materials for biochar high calorific value of co-firing applications

The Filter Inlet for Gases and AEROsols coupled to a Chemical Ionization Mass Spectrometer (FIGAERO-CIMS) can be used to derive volatility of atmospheric aerosol by using the temperature at thermogram maximum signal (Tmax). For complex ambient particle matrices, Tmax of an individual compound often varies, for reasons not fully elucidated. Here, we apply machine learning to study the relation between Tmax of levoglucosan (C6H10O5), a common tracer to identify the influence of biomass burning (BB) in ambient air, and a set of atmospheric and instrumental parameters for an ambient year-long FIGAERO-CIMS data set measured in the Arctic. Using three different modeling approaches, namely, multiple linear regression (MLR), random forest (RF) regressor, and XGBoost regressor, we find that the mass loading on the FIGAERO filter has the highest relevance for variation in Tmax of levoglucosan. On the basis of these results, we suggest controlling the mass collected on the filter for continuous online measurement with the FIGAERO-CIMS if quantitative volatility information is to be gained. More generally, we demonstrate the usefulness of machine learning approaches for characterization of instrumental backgrounds in complex ambient or laboratory data.

Morphological and geochemical characterization of the particulate depositions collected from the surfaces of urban monuments located in two Mediterranean cities characterized by high levels of atmospheric particulate matter (PM), namely Thessaloniki in Greece and Nicosia in Cyprus, was carried out using multiple analytical techniques. The geochemical composition of bulk samples was found to be dominated by CaO and SiO2 with lower proportions of Al2O3, Fe2O3, and MgO suggesting influence from local and/or transported dust (road dust, Saharan dust). The effect of traffic was evident on the sides of monuments oriented towards busy roads with higher concentrations of anthropogenic elements such as Zn, Ba, Cu, V, and Ni. Gypsum was detected in a few samples only and is possibly attributed to sulfation process and/or windblown Saharan dust. Notably, a wide spectrum of low- and high-molecular-weight organic compounds was also detected (aliphatic hydrocarbons and organic compounds containing carbonyl- and carboxyl-groups, urea and nitrogen-containing biomass, amino acids, fatty acids and lipids, plant residues, synthetic polymer residues, and combustion products) suggesting the presence of various natural and anthropogenic sources. The results obtained in the study may serve as a guide for the development of appropriate strategies for protection and conservation of the urban architectural heritage in the two countries.

The altitude-toxicity paradox: Biodiesel blends reduce particulate mass but elevate calculated PAH Toxic equivalency under simulated highland conditions.

Although several methods have been developed for the detection of total per- and polyfluoroalkyl substances (PFAS), most of them are not applicable for rapid site characterization. We report a combustion-gas analytical approach for PFAS, offering a simple, robust, and field-deployable method for PFAS screening in soil. We demonstrate that PFAS with varying functional groups and structures are combusted to SiF4 and HF, whereas inorganic fluorine forms only HF. This distinction overcomes a major challenge of inorganic fluorine interference in total PFAS analysis, as SiF4 can be selectively quantified by FTIR. We then applied the method to PFAS measurement in soil samples. Soil was extracted with basic methanol, which further minimized interference from inorganic fluorine and enabled semiquantitative detection, and the extracts were combusted for analysis. The method detection limits ranged from 13 to 16 μgPFAS/gsoil for three PFAS, with relative standard deviations of 34-47% at concentrations near the detection limit, indicating the current applicability of the method as a screening tool rather than quantitation, pending further process development. The developed screening method and tool has the capability to directly screen for PFAS at contaminated sites, which often have total PFAS concentrations between 10 and >100 μg/g.

The multilayer thermite material Al/CuO is an outstanding representative of metastable intermolecular composites characterized by excellent energy and combustible properties. However, the combustion of such a material is accompanied by intensive release of gas and spraying products. In this work, it is demonstrated that in a certain thickness range (2-3μm) of the multilayer thermite material (Al/CuO)nformed on the surface of Sitall substrates, the reaction products remain on the surface of the substrate in the form of composite entities, in some cases resembling ladybugs. By means of high-speed video shooting from different angles, the propagation velocity of the combustion front was measured and the intensity of gas release was visually assessed depending on the thickness of the multilayer structure (Al/CuO)n. The spraying products effect becomes significant for multilayer structures (Al/CuO)40with a total thickness of 4μm. It was found that the propagation velocity of the combustion front increases with the thickness of the multilayer structure (Al/CuO)nuntil the effect of spraying products intensifies. The morphology and composition of the combustion products of multilayer structures were studied using scanning electron microscopy in combination with focused ion beam and energy-dispersive x-ray spectroscopy. In particular, it was found that in many cases, the composite entities are hollow inside and consist of an aluminum oxide framework, rounded fuses of metallic copper, and fragments of unreacted film multilayers. As a result of the analysis of experimental data, two combustion modes of multilayer thermocomposite materials on a substrate were described depending on the temperature of the combustion front, which are related to the thickness of the multilayer structure.

Fly ash (FA) is a coal combustion product with variable mineral composition, high alkalinity, and elevated enrichment of heavy metals (HMs) such as As, Se, Mo, Cd, and Pb. Fly ash greatly influences soil dynamics by altering soil pH, nutrient mobility, microbial activity, soil structure, and texture. This review evaluates phytoextraction as a sustainable and eco-friendly strategy for remediating FA-contaminated soils. It explores the physicochemical properties of FA, the impact of FA and associated heavy metals (HMs) on soil, the mechanisms of HM hyperaccumulation in plants, and the effectiveness of phytoextraction based on the bioaccumulation factor (BAF) and translocation factor (TF). Case studies from various regions demonstrate the great potential of hyperaccumulator species to extract toxic HMs from FA-impacted soils. However, challenges such as low metal bioavailability, limited field validation, and inadequate management of contaminated biomass hinder large-scale application. Future research should focus on optimizing biomass utilization, developing comprehensive hyperaccumulator databases, and advancing genetic and policy frameworks to enhance the scalability and effectiveness of phytoextraction.

Combustion of biomass in existing coal-fired power plants is a promising near-term option for reducing greenhouse gas emissions—particularly when combined with oxy-fuel combustion and subsequent Carbon Capture and Storage (CCS). However, the scale-up of biomass oxy-fuel technology is hampered by the lack of high-fidelity experimental data from combustion chambers of industrially relevant thermal loads. In large-scale facilities, the application of advanced diagnostics is often restricted by limited optical access and harsh conditions, which is why many previous studies rely on conventional probe-based measurements. This study demonstrates the successful transfer of planar Particle Tracking Velocimetry (PTV) and direct Tunable Diode Laser Absorption Spectroscopy (TDLAS) to a semi-industrial combustion chamber. Experiments were conducted for air- and oxy-fuel atmospheres with oxygen volume fractions ranging from 27 % to 33 %, each under two swirl settings. PTV measurements delivered two-dimensional fields of the solid fuel particle velocities, while TDLAS provided information on gas-phase temperatures along several beam paths. The data presented in this work are the first of their kind for a semi-industrial biomass combustor, as comparable datasets were previously limited to laboratory-scale, optically accessible systems. The results indicate that the global particle velocity field and particle distribution are governed primarily by the swirl, with oxygen fraction causing secondary effects. For both swirl settings, the oxy-fuel case at 33 % O 2 most closely matched the corresponding air-fired particle velocity field. TDLAS measurements captured swirl-dependent temperature patterns. The acquired data provide a valuable basis for the validation of numerical simulations and for the design and optimization of future combustors. • Investigation of biomass combustion in semi-industrial 1 MW th combustion chamber. • Combustion in air and oxy-fuel atmospheres (27 % to 33 % O 2 ), with variation of swirl. • Measurement of particle velocities using 2D-PTV and gas temperature using TDLAS. • Flow field is dominated by swirl, oxygen fraction has secondary influence. • Flow field in an oxy-fuel atmosphere of 33 % O 2 is most similar to air-firing.

Incineration of PFBA with temperature: Reaction mechanisms, kinetics, and residence time from molecular level simulations.

ABSTRACT With the acceleration of urbanization and the deepening of low-carbon transformation, the development of underground space has become an inevitable trend in urban development. Leaks in hydrogen-blended natural gas pipelines pose significant explosion risks to urban underground spaces containing numerous rigid and flexible facilities. This study conducts experiments on hydrogen-methane blended semi-open pipelines, focusing on analyzing the protective effect of flexible facilities placed in front of rigid facilities against explosions of 20% hydrogen-methane blends within underground spaces. Findings revealed that at constant blockage rates, smaller spacing enhances the explosion attenuation effect of flexible facilities, while increased spacing elevates explosion hazards. At constant spacing, when FBR < RBR, the maximum explosion overpressure attenuation rate reached 53.83%. However, excessively high flexible blockage rates (FBR > RBR) increased overpressure by 166.67%. Upstream pressure oscillations exceeded downstream levels, with Helmholtz oscillation frequencies generally decreasing while exhibiting intermittent fluctuations. Downstream overpressure rise rates showed more pronounced periodic oscillations. Flexible facility blockage rate exerted a greater influence on explosion intensity index than spacing, indicating that closer layouts with lower blockage rates offer enhanced safety. These findings provide a theoretical basis for optimizing rigid-flexible facility layout design, thereby mitigating combustible gas explosion hazards and improving safety.

Synergistic Co-pyrolysis of Biomass and Waste Plastics for Enhanced Combustible Gas Production: A Comprehensive Review

ABSTRACT The strategy of substituting combustion air with sintering flue gas in circulating fluidized bed (CFB) systems provides a low‐cost and highly efficient approach for the purification of sintering off‐gas. In this work, coke was employed as the reducing agent, and a high‐temperature vertical tube furnace was used to simulate the dense‐phase region of a CFB to examine the reduction of NO by coke over the temperature range of 750°C–950°C. Research findings indicate that as the temperature increases, the NO reduction efficiency by coke rises significantly from 0.8% to 44.5%. With a gradual increase in NO feed concentration, the NO conversion rate at 950°C progressively decreases to 41.1%. The addition of CO enhances NO conversion, with higher CO feed concentrations leading to greater NO conversion rates. At 950°C, as the CO 2 concentration increases, the NO conversion rate gradually declines from approximately 42% to 30%, suggesting that CO 2 inhibits the NO reduction reaction by coke. This study systematically analyzes the reaction characteristics of NO with coke within the typical oxygen concentration range of sintering flue gas, providing an experimental basis for the co‐processing of sintering flue gas in CFB.

Measurements of sulfuric acid (H2SO4) and sulfur trioxide (SO3) were conducted in Pittsburgh, Pennsylvania, during field campaigns in Fall 2023 and Fall 2024. These measurements identified nocturnal concentrations of H2SO4 comparable to those of daytime values. Nocturnal H2SO4 concentrations were observed to increase by 5 × 105 to 5 × 107 molecules cm-3 above background on 16 of the 31 measurement nights. The median peak concentration during events was 6.5 × 106 molecules cm-3, with a maximum of 1.0 × 108 molecules cm-3, exceeding previously reported nighttime concentrations. Increases in H2SO4 concentrations were positively correlated with the anomalously high SO3 concentrations and condensation sink rates, indicating that the formation of H2SO4 increased to overcome the loss rates to particles. Increases in particulate mass and the mass fraction of metals commonly emitted from coal combustion and steel production were also observed. The air masses were traced back to the southeast of Pittsburgh, a region home to a steel mill, coke plant, and a steel processing plant. The observations indicate a previously unrecognized nighttime formation pathway for H2SO4, potentially from heterogeneous catalysis with metal or black carbon, originating from steel and coke plant emissions. Further measurements are needed to identify key compounds and chemical processes driving these increases in nocturnal H2SO4 concentrations.

ABSTRACT Over the past few decades, wildland fires have increased in frequency and severity across many regions of the world, particularly in the western United States. Smoldering combustion of solid fuels, a heterogeneous combustion mode that is particularly difficult to detect and suppress, plays an important role in fire persistence, emission generation, and the potential transition to flaming under favorable conditions. In this study, a wind tunnel setup was developed capable of investigating combustion behavior and emissions of different fuels found in wildland and urban areas under well-defined boundary conditions. Douglas fir lumber, a common softwood species found in the wildland and used in home structures in the US, was examined using three fuel sizes corresponding to volume-to-surface area ratios (V/SA) of 3.8 mm, 4.2 mm, and 5.4 mm, under constant heat power inputs of 100 W and 200 W and wind speeds ranging from 0.5 m/s to 1.5 m/s. Ignition was performed using a nichrome wire system from the bottom of the samples, while the exposed surfaces were subjected to a controlled crossflow of air. The mass loss rate of the fuels undergoing combustion was tracked using a precision mass balance, along with measurements of aerosol and major gaseous combustion product species. The experiments produced a combustion regime map, showing that fuel size and ignition power strongly influenced burning rates and observed combustion behavior. Larger V/SAs led to stronger burning rates and a greater propensity for the observed smoldering-to-flaming (StF) transition, accompanied by lower particulate matter emissions. Gaseous product emissions and modified combustion efficiency demonstrated correlations between carbon monoxide and carbon dioxide fractions and combustion behavior. The experimental data were compared with simplified theoretical analyses and found to be in reasonable agreement. This work enhances the understanding of the smoldering combustion behavior of a common solid fuel relevant to wildland and structural fire scenarios, providing experimental data obtained under well-defined boundary conditions, useful for emission characterization and model development.

Abstract - Indoor air pollution poses a severe and growing public health risk, with household environments frequently experiencing elevated concentrations of volatile organic compounds, combustible gases, and smoke due to cooking, cleaning activities, and inadequate ventilation. Conventional air purifiers operate on fixed schedules without awareness of actual pollutant concentrations, resulting in energy wastage during safe periods and insufficient purification during pollution events. This paper presents a real-time air quality monitoring and smart filtration system that integrates MQ2, MQ4, and MQ135 gas sensors with an ESP8266/ESP32 microcontroller performing edge-based machine learning inference for intelligent, adaptive filtration control. The system converts raw sensor readings to PPM concentrations and calculates a composite Air Quality Index (AQI). Five machine learning techniques are applied: Linear Regression for AQI prediction, Decision Tree for smart filter speed control, Isolation Forest for anomaly detection, ML-based sensor calibration correction, and time-based pattern prediction. Results are displayed locally on an LCD and simultaneously uploaded to Firebase Realtime Database feeding a web dashboard with five live real-time graphs. The HEPA H13 filter is controlled automatically with variable fan speed (0–100%) based on AQI classification and ML recommendations. Experimental validation confirms AQI prediction R² above 0.87, Decision Tree filter control accuracy above 90%, and average system response time under 3.5 seconds — all achieved at a total hardware cost of approximately Rs. 3,000. Key Words: Air Quality Index, MQ2, MQ4, MQ135, ESP32, ESP8266, Machine Learning, Linear Regression, Decision Tree, Isolation Forest, Firebase, HEPA Filter, Edge Computing, IoT, Smart Filtration.

Iron fuel is a promising high‐energy density energy carrier. Using recycled iron‐rich sources, such as Direct Reduced Iron (DRI) ores, is of strong economic interest considering the added costs from purifying iron, but the influence of oxidized additions (Si, Al, Mn oxides) on the combustion process has hardly been investigated. In this study, the combustion products of DRI powder is compared to that of pure Fe in an open propane flame. The microstructure and chemical changes are characterized at different scales using a combination of SEM‐EDS and STEM‐EDS. Pore formation in combusted DRI and pure Fe powders is compared using X‐ray computed tomography (CT), BET surface area and gas pycnometer measurements, together with laser diffraction and image analysis granulometry. The role of oxygen gas release on the pore formation and the major modifications brought by the presence of Si are unveiled for the first time by a combination of experimental characterization and thermodynamic simulations. Crucially, our findings show that DRI is a viable source of Fe fuel. This paves the way for more sustainable and economically competitive high‐energy‐density carriers.

Soot and black carbon (BC) are typically regarded as troublesome products of incomplete combustion; however, growing interest in circular economy strategies and sustainable manufacturing highlights their potential as secondary functional carbon materials, including additive manufacturing (AM). This review synthesises the recovery, upgrading, and valorization pathways for soot/BC and recovered carbon black (rCB), with a particular focus on streams captured by mandatory emission-control systems (e.g., diesel/gasoline particulate filters, electrostatic precipitators, baghouse filters, and chimney soot) and the requirements for transforming these heterogeneous residues into reproducible AM feedstocks. A two-stage approach was applied, combining (i) an analysis of the European Union regulatory context (waste classification, end-of-waste routes, and chemical safety obligations, including REACH) with (ii) a structured literature review of studies published in 2017–2026 indexed in the Web of Science and Scopus, culminating in a qualitative synthesis of 152 papers. Evidence indicates that scale-up is primarily constrained by strong compositional variability and contaminant burdens (ash, metals, and PAHs), which affect dispersion, rheology, and property reproducibility, necessitating robust standardisation and risk assessment. This review maps key preparation and upgrading strategies (e.g., classification, ash/metal reduction, and control of organic fractions) and discusses their relevance across AM routes such as FDM/FFF, SLS, DLP, and DIW. Overall, realising credible waste-to-value pathways requires aligning technical performance targets with regulatory compliance and developing consistent characterisation protocols to enable the safe and predictable use of soot/rCB-derived fillers in AM.

The article describes a basic IoT framework through the integration of ESP 323 samples, an array for several sensors, as well as an ensemble of machine learning algorithms for both environmental monitoring and disaster prediction. The IoT monitoring system incorporates MQ series gas sensors (MQ-135, MQ-2, MQ-7, MQ-9), ultrasonic distance sensors, and the OpenWeather API to monitor the environment for air quality (e.g. smoke detection), carbon monoxide levels and combustible gas levels, water level and seismic activity. One of the most significant advantages of this proposal is the machine learning pipeline, which consists of a total of ten separate classifiers and includes: Random Forest, Gradient Boosting, Support Vector Machines, and Neural Networks. The use of the ensemble models gives 94% or higher accuracy in predicting flooding events. Other features that are part of the proposed solution include: automated email alerting, real time visualization of sensor data via Web Dashboard, access to sensor data via ThingSpeak cloud service, and ability to analyze both historical and current data. The experimental testing indicates the ability of the proposed system to detect hazards using configurable alert thresholds, and 15 minutes of alert cool down period to avoid generating too many alerts (alert fatigue). Overall, the proposed architecture can provide a cost-effective, scalable solution to encompass both environmental monitoring and disaster early warning systems.