Adsorption removal of mercury from flue gas by metal selenide: A review.

Mercury (Hg) pollution has been a global concern in recent decades, posing a significant threat to entire ecosystems and human health due to its cumulative toxicity, persistence, and transport in the atmosphere. The intense interaction between mercury and selenium has opened up a new field for studying mercury removal from industrial flue gas pollutants. Besides the advantages of good Hg° capture performance and low secondary pollution of the mineral selenium compounds, the most noteworthy is the relatively low regeneration temperature, allowing adsorbent regeneration with low energy consumption, thus reducing the utilization cost and enabling recovery of mercury resources. This paper reviews the recent progress of mineral selenium compounds in flue gas mercury removal, introduces in detail the different types of mineral selenium compounds studied in the field of mercury removal, reviews the adsorption performance of various mineral selenium compounds adsorbents on mercury and the influence of flue gas components, such as reaction temperature, air velocity, and other factors, and summarizes the adsorption mechanism of different fugitive forms of selenium species. Based on the current research progress, future studies should focus on the economic performance and the performance of different carriers and sizes of adsorbents for the removal of Hg0 and the correlation between the gas-particle flow characteristics and gas phase mass transfer with the performance of Hg0 removal in practical industrial applications. In addition, it remains a challenge to distinguish the oxidation and adsorption of Hg0 quantitatively.

Research status and prospect of purification technology of sulfur-containing odor gas.

Catalytic purification of sulphur-containing malodorous gases has attracted wide attention because of its advantages of high purification efficiency, low energy consumption and lack of secondary pollution. The selection of efficient catalysts is the key to the problem, while the preparation and optimisation of catalysts depend on the analysis of experimental results and in-depth mechanistic analysis. By analysing the published literature, bibliometric analysis can identify existing research hotspots, the areas of interest and predict development trends, which can help to identify hot catalysts in the catalytic purification of sulphur-containing odours and to investigate their catalytic purification mechanisms. Therefore, this paper uses bibliometric analysis, based on Web Of Science and CNKI databases, CiteSpace and VOS viewer software to collate and analyse the literature on the purification of sulphur-containing odour pollutants, to identify the current research hotspots, to summarise the progress of research on the catalytic purification of different types of sulphur-containing odours, and to analyse their reaction mechanisms and kinetics. On this basis, the research progress of catalytic purification of different kinds of sulfur odour is summarized, and the reaction mechanism and dynamics are summarized.

Multi-gas pollutant detection based on sparrow search algorithm optimized ALSTM-FCN.

It is critical to identify and detect hazardous, flammable, explosive, and poisonous gases in the realms of industrial production and medical diagnostics. To detect and categorize a range of common hazardous gasses, we propose an attention-based Long Short term memory Full Convolutional network (ALSTM-FCN) in this paper. We adjust the network parameters of ALSTM-FCN using the Sparrow search algorithm (SSA) based on this, by comparison, SSA outperforms Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), Gray Wolf Optimization (GWO) Algorithm, Cuckoo Search (CS) Algorithm and other traditional optimization algorithms. We evaluate the model using University of California-Irvine (UCI) datasets and compare it with LSTM and FCN. The findings indicate that the ALSTM-FCN hybrid model has a better reliability test accuracy of 99.461% than both LSTM (89.471%) and FCN (96.083%). Furthermore, AdaBoost, logistic regression (LR), extra tree (ET), decision tree (DT), random forest (RF), K-nearest neighbor (KNN) and other models were trained. The suggested approach outperforms the conventional machine learning model in terms of gas categorization accuracy, according to experimental data. The findings indicate a potential for a broad range of polluting gas detection using the suggested ALSTM-FCN model, which is based on SSA optimization.

Study of LFG bubble accumulation and discontinuous flow in the highly saturated region of landfill below the leachate level.

Landfills in developing countries are typically characterized by high waste water content and elevated leachate levels. Despite the ongoing biodegradation of waste in the highly saturated regions of these landfills, which leads to gas accumulation and bubble formation, the associated gas pressure that poses a risk to landfill stability is often overlooked. This paper introduces a landfill gas (LFG) bubble generation model and a two-fluid model that considers bubble buoyancy and porous medium resistance. The entire process can be divided into two stages based on the force balance and velocity of bubbles: Bubble Development Stage and the Two-Fluid Flow Stage. The models were validated using a one-dimensional analytical solution of hydraulic distribution that considers bubble generation, as well as an experiment involving air injection into a saturated medium. The mechanisms of LFG accumulation and ascent, leachate level rise, and discontinuous leachate-gas flow were then investigated in conjunction with continuous flow in the unsaturated region. The results indicate that the generation of LFG bubbles below the leachate level can cause a rise in the level height of more than 20%. During the Bubble Development Stage, there is a critical height for bubble ascent, above which the buoyancy exceeds the combined forces of gravity and resistance, resulting in less than 10% of bubbles continuously flowing into the unsaturated zone for recovery. The developed model effectively captures the accumulation and flow of LFG bubbles below the leachate level and could be further utilized to study leachate-gas pumping in the future.

Investigation of gaseous end products produced by thulium fiber laser lithotripsy of cystine, uric acid, and calcium oxalate monohydrate stones: A gas chromatographic and electron microscopic analysis.

Laser lithotripsy mechanisms can cause the chemical decomposition of stone components and the emergence of different end products. However, the potentially toxic end products formed during thulium fiber laser (TFL) lithotripsy of cystine stones have not been sufficiently investigated. The aim of our in vitro study is to analyze the chemical content of the gas products formed during the fragmentation of cystine stone with TFL. Human renal calculi consisting of 100% pure cystine, calcium oxalate monohydrate, or uric acid were fragmented separately with TFL in experimental setups and observed for gas release. After the lithotripsy, only the cystine stones showed gas formation. Gas chromatography-mass spectrometry was used to analyze the gas qualitatively, and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) and X-ray diffraction was used to examine the dried cystine stone fragments. Fragmentation of the cystine stones released free cystine, sulfur, hydrogen sulfide, and carbon disulfide gas. The SEM-EDX and X-ray diffraction analyses revealed that the free cystine in the dried fragments contained 43.1% oxygen, 28.7% sulfur, 16.1% nitrogen, and 12.1% carbon atoms according to atomic weight. The detection of potentially toxic gases after lithotripsy of cystine stones with TFL indicates a risk of in vivo production. Awareness needs to be increased among healthcare professionals to prevent potential inhalation and systemic toxicity for patients and operating room personnel during TFL lithotripsy of cystine stones.

Anode potential regulates gas composition and microbiome in anaerobic electrochemical digestion.

Anaerobic electrochemical digestion (AED) is an effective system for recovering biogas from organic wastes. However, the effects of different anode potentials on anaerobic activated sludge remain unclear. This study confirmed that biofilms exhibited the best electroactivity at -0.2 V (vs. Ag/AgCl) compared to -0.4 V and 0 V. Gas was further regulated, with the highest hydrogen content (47 ± 7 %) observed at -0.2 V. The 0 V system produced the largest amount of methane (70 ± 8 %) and exhibited the greatest presence of hydrogen-utilizing microorganisms. The gas yield at -0.4 V was the lowest, with no hydrogen detected. Excess bioelectrohydrogen at -0.2 V and 0 V caused the co-enrichment of Methanobacterium and Acetoanaerobium, establishing a thermodynamically feasible current-acetate-hydrogen electron cycle to improve electrogenesis. These results provide insights into the regulatory strategies of MEC technology during anaerobic digestion, which play a decisive role in determining the composition of biogas.

Catalytic Ozonation of Low Concentration Toluene over MnFeOx-USY Catalyst: Effects of Interactions between Catalytic Components and Introduction of Gas Phase NOx.

A series of Mn and Fe metal oxide catalysts loaded onto USY, as well as single metal oxides, were prepared and characterized. The effects of interactions between the catalytic components and the introduction of gas phase NO on the catalytic ozonation of toluene were investigated. Characterization showed that there existed strong interactions between MnOx, FeOx, and USY, which enhanced the content of oxygen vacancies and acid sites of the catalysts and thus boosted the generation of reactive oxygen species and the adsorption of toluene. The MnFeOx-USY catalyst with MnOx and FeOx dimetallic oxides exhibited the most excellent performance of catalytic ozonation of toluene. On the other hand, the presence of NOx in reaction gas mixtures significantly promoted both toluene conversion and mineralization, which was attributed to the formation of nitrate species on the catalysts surface and thus the increase of both acid sites and toluene oxidation sites. Meanwhile, the reaction mechanism between O3 and C7H8 was modified in which the strong interactions between MnOx, FeOx, and USY accelerated the reaction progress based on the L-H route. In addition, the formation of the surface nitrate species not only promoted reaction progress following the L-H route but also resulted in the occurrence of the reaction via the E-R route.

Electronic Noses: From Gas-Sensitive Components and Practical Applications to Data Processing.

Artificial olfaction, also known as an electronic nose, is a gas identification device that replicates the human olfactory organ. This system integrates sensor arrays to detect gases, data acquisition for signal processing, and data analysis for precise identification, enabling it to assess gases both qualitatively and quantitatively in complex settings. This article provides a brief overview of the research progress in electronic nose technology, which is divided into three main elements, focusing on gas-sensitive materials, electronic nose applications, and data analysis methods. Furthermore, the review explores both traditional MOS materials and the newer porous materials like MOFs for gas sensors, summarizing the applications of electronic noses across diverse fields including disease diagnosis, environmental monitoring, food safety, and agricultural production. Additionally, it covers electronic nose pattern recognition and signal drift suppression algorithms. Ultimately, the summary identifies challenges faced by current systems and offers innovative solutions for future advancements. Overall, this endeavor forges a solid foundation and establishes a conceptual framework for ongoing research in the field.

Gas-Sensing Performance of Metal Oxide Heterojunction Materials for SF6 Decomposition Gases: A DFT Study.

The online monitoring of GIS equipment can be realized through detecting SF6 decomposition gasses. Metal oxide heterojunctions are widely used as gas-sensing materials. In this study, the structural and electrical properties of In2O3-ZnO and TiO2-ZnO heterojunctions were analyzed based on density functional theory calculations. After heterojunction structural optimization, the electrical conductivity of these two heterojunctions was enhanced compared to each intrinsic model, and the electrical conductivity is ranked as follows: In2O3-ZnO heterojunction > TiO2-ZnO heterojunction. The gas-sensing response of these two heterojunctions to four SF6 decomposition gasses, H2S, SO2, SOF2, and SO2F2, was investigated. For gas adsorption systems, the adsorption energy, charge transfer, density of states, charge difference density, and frontier molecular orbitals were calculated to analyze the adsorption and gas-sensing performance. For gas adsorption on the In2O3-ZnO heterojunction surface, the induced conductivity changes are in the following order: H2S > SO2F2 > SOF2 > SO2. For gas adsorption on the TiO2-ZnO heterojunction surface, H2S and SOF2 increase conductivity, and SO2 and SO2F2 decrease conductivity.

Is reoperation required for patients presenting with hepatic portal venous gas after gastrointestinal surgery: a review of the literature.

BACKGROUND AND OBJECTIVE: Hepatic portal venous gas(HPVG) represents a rare radiographic phenomenon frequently linked to intestinal necrosis, historically deemed to need immediate surgical intervention. The pivotal query arises about the imperative of urgent surgery when a patient manifests HPVG after gastrointestinal surgery. This inquiry seeks to elucidate whether emergent surgical measures remain a requisite in such cases. METHODS: The investigation into 14 cases of HPVG after gastrointestinal procedures was conducted through a comprehensive review of relevant literature. This methodological approach contributes to a nuanced understanding of HPVG occurrences following gastrointestinal surgery, informing clinical considerations and potential therapeutic strategies. RESULTS: Among the 14 patients, 12 recovered and 2 died. 6 patients underwent surgical exploration, 4 with negative findings and recovered. 8 cases received conservative treatment, resulting in improvement for 5, and 1 initially treated conservatively, revealed perforation during later surgical exploration, leading to improvement, 1 case ended in mortality. CONCLUSION: After gastrointestinal surgery, in Computed Tomography (CT) imaging, the coexistence of HPVG and gastrointestinal dilatation, without signs of peritoneal irritation on abdominal examination, may suggest HPVG due to acute gastrointestinal injury, intestinal gas, and displacement of gas-producing bacteria. These patients can be managed conservatively under close supervision. In cases where HPVG coexists with gastrointestinal dilatation and Pneumatosis intestinalis (PI) without signs of peritoneal irritation, conservative treatment may be continued under close supervision. However, if progressive exacerbation occurs despite close monitoring and the aforementioned treatments, timely surgical exploration is deemed necessary. When HPVG is combined with signs of peritoneal irritation, prompt laparotomy and exploration are preferred.

Intestinal gases as a non-invasive measurement of microbial fermentation and host health.

Microbial fermentation and associated products provide insights into the gut microbiota-host relationship. Here, we propose using improved technologies that allow non-invasive, real-time measurements of intestinal gases as a metric for microbial fermentation. This approach has the potential to provide a basis for personalized interventions that improve host metabolic health.

Yolk-Shell Hierarchical Pore Au@MOF Nanostructures: Efficient Gas Capture and Enrichment for Advanced Breath Analysis.

Surface-enhanced Raman scattering (SERS) offers a promising, cost-effective alternative for the rapid, sensitive, and quantitative analysis of potential biomarkers in exhaled gases, which is crucial for early disease diagnosis. However, a major challenge in SERS is the effective detection of gaseous analytes, primarily due to difficulties in enriching and capturing them within the substrate's "hotspot" regions. This study introduces an advanced gas sensor combining mesoporous gold (MesoAu) and metal-organic frameworks (MOFs), exhibiting high sensitivity and rapid detection capabilities. The MesoAu provides abundant active sites and interconnected mesopores, facilitating the diffusion of analytes for detection. A ZIF-8 shell enveloping MesoAu further enriches target molecules, significantly enhancing sensitivity. A proof-of-concept experiment demonstrated a detection limit of 0.32 ppb for gaseous benzaldehyde, indicating promising prospects for the rapid diagnosis of early stage lung cancer. This research also pioneers a novel approach for constructing hierarchical plasmonic nanostructures with immense potential in gas sensing.

Design of eco-friendly antifreeze peptides as novel inhibitors of gas-hydration kinetics.

In this study, peptides designed using fragments of an antifreeze protein (AFP) from the freeze-tolerant insect Tenebrio molitor, TmAFP, were evaluated as inhibitors of clathrate hydrate formation. It was found that these peptides exhibit inhibitory effects by both direct and indirect mechanisms. The direct mechanism involves the displacement of methane molecules by hydrophobic methyl groups from threonine residues, preventing their diffusion to the hydrate surface. The indirect mechanism is characterized by the formation of cylindrical gas bubbles, the morphology of which reduces the pressure difference at the bubble interface, thereby slowing methane transport. The transfer of methane to the hydrate interface is primarily dominated by gas bubbles in the presence of antifreeze peptides. Spherical bubbles facilitate methane migration and potentially accelerate hydrate formation; conversely, the promotion of a cylindrical bubble morphology by two of the designed systems was found to mitigate this effect, leading to slower methane transport and reduced hydrate growth. These findings provide valuable guidance for the design of effective peptide-based inhibitors of natural-gas hydrate formation with potential applications in the energy and environmental sectors.

A nonparametric mixed exponentially weighted moving average-moving average control chart with an application to gas turbines.

This study aims to develop a nonparametric mixed exponentially weighted moving average-moving average (NPEWMA-MA) sign control chart for monitoring shifts in process location, particularly when the distribution of a critical quality characteristic is either unknown or non-normal. In literature, the variance expression of the mixed exponentially weighted moving average-moving average (EWMA-MA) statistic is calculated by allowing sequential moving averages to be independent, and thus the exclusion of covariance terms results in an inaccurate variance expression. Furthermore, the effectiveness of the EWMA-MA control chart deteriorates when the distribution of a critical quality characteristic deviates from normality. The proposed NPEWMA-MA sign control chart addresses these by utilizing the corrected variance of the EWMA-MA statistic and incorporating the nonparametric sign test into the EWMA-MA charting structure. The chart integrates the moving average (MA) statistic into the exponentially weighted moving average (EWMA) statistic. The EWMA-MA charting statistic assigns more weight to recent w samples, with weights for previous observations decling exponentially. Monte Carlo simulations assess the chart's performance using various run length (RL) characteristics such as average run length (ARL), standard deviation of run length (SDRL), and median run length (MRL). Additional measures for overall performance include the average extra quadratic loss (AEQL) and relative mean index (RMI). The proposed NPEWMA-MA sign control chart demonstrates superior performance compared to existing nonparametric control charts across different symmetrical and asymmetric distributions. It efficiently detects process shifts, as validated through both a simulated study and a real-life example from a combined cycle power plant.

A machine learning-based electronic nose system using numerous low-cost gas sensors for real-time alcoholic beverage classification.

This study introduces numerous low-cost gas sensors and a real-time alcoholic beverage classification system based on machine learning. Dogs possess a superior sense of smell compared to humans due to having 30 times more olfactory receptors and three times more olfactory receptor types than humans. Thus, in odor classification, the number of olfactory receptors is a more influential factor than the number of receptor types. From this perspective, this study proposes a system that utilizes distinctive data patterns resulting from heterogeneous responses among numerous low-cost homogeneous MOS-based sensors with poor gas selectivity. To evaluate the performance of the proposed system, learning data were gathered using three alcoholic beverage groups including different aged whiskeys, Korean soju with 99% same compositions, and white wines made from the Sauvignon blanc variety, sourced from various countries. The electronic nose system was developed to classify alcoholic samples measured using 30 gas sensors in real time. The samples were injected into a gas chamber for 60 seconds, followed by a 60-second injection of clean air. After preprocessing the time-series data into four distinct datasets, the data were analyzed using a machine learning algorithm, and the classification results were compared. The results showed a high classification accuracy of over 99%, and it was observed that classification performance varied depending on data preprocessing. As the number of gas sensors increased, the prediction accuracy improved, reaching up to 99.83 ± 0.21%. These experimental results indicated that the proposed electronic nose system's classification performance was comparable to that of commercial electronic nose systems. Additionally, the implementation of an alcoholic beverage classification system based on a pretrained LDA model demonstrated the feasibility of real-time classification using the proposed system.

Improved medical waste plasma gasification modelling based on implicit knowledge-guided interpretable machine learning.

Ensuring the interpretability of machine learning models in chemical engineering remains challenging due to inherent limitations and data quality issues, hindering their reliable application. In this study, a qualitatively implicit knowledge-guided machine learning framework is proposed to improve plasma gasification modelling. Starting with a pre-trained machine learning model, parameters are further optimized by integrating the heuristic algorithm to minimize the data fitting errors and resolving implicit monotonic inconsistencies. The latter is comprehensively quantified through Monte Carlo simulations. This framework is adaptive to different machine learning techniques, exemplified by artificial neural network (ANN) and support vector machine (SVM) in this study. Validated by a case study on plasma gasification, the results reveal that the improved models achieve better generalizability and scientific interpretability in predicting syngas quality. Specifically, for ANN, the root mean square error (RMSE) and knowledge-based error (KE) reduce by 36.44% and 83.22%, respectively, while SVM displays a decrease of 2.58% in RMSE and a remarkable 100% in KE. Importantly, the improved models successfully capture all desired implicit monotonicity relationships between syngas quality and feedstock characteristics/operating parameters, addressing a limitation that traditional machine learning struggles with.

External gas-assisted mold temperature control and optimization molding parameters for improving weld line strength in polyamide plastics.

In this study, we present a novel approach to injection molding, focusing on the strength of weld lines in polyamide 6 (PA6) composite samples. By implementing a mold temperature significantly higher than the typical molding practice, which rarely exceeds 100°C, we assess the effects of advanced mold temperature management. The research introduces a newly engineered mold structure specifically designed for localized mold heating, distinguishing it as the 'novel cavity.' This innovative design is compared against traditional molding methods to highlight the improvements in weld line strength at elevated mold temperatures. To optimize the molding parameters, we apply an Artificial Neural Network (ANN) in conjunction with a Genetic Algorithm (GA). Our findings reveal that the optimal ultimate tensile strength (UTS) and elongation values are achieved with a filling time of 3.4 seconds, packing time of 0.8 seconds, melt temperature of 246°C, and a novel high mold temperature of 173°C. A specific sample demonstrated the best molding parameters at a filling time of 3.4 seconds, packing time of 0.4 seconds, melt temperature of 244°C, and mold temperature of 173°C, resulting in an elongation value of 582.6% and a UTS of 62.3 MPa. The most influential factor on the PA6 sample's UTS and elongation at the weld line was found to be the melt temperature, while the filling time had the least impact. SEM analysis of the fracture surfaces revealed ductile fractures with rough surfaces and grooves, indicative of the weld line areas' bonding quality. These insights pave the way for significant improvements in injection molding conditions, potentially revolutionizing the manufacturing process by enhancing the structural integrity of the weld lines in molded PA6 samples.

Customizable Colorimetric Sensor Array via a High-Throughput Robot for Mitigation of Humidity Interference in Gas Sensing.

One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.

Response Enhancement in High-Temperature H2S-Treated Metal Oxide Gas Sensors.

Metal oxide gas sensors (MOGS), crucial components in monitoring air quality and detecting hazardous gases, are well known for their poisoning effects when exposed to certain gas molecules, such as hydrogen sulfide. Surprisingly, our research reveals that high-temperature H2S treatment leads to an enhancement effect rather than response decay. This study investigates the time-decaying response enhancement, being attributed to the formation of metal sulfide and metal sulfate on the metal oxide's surface, enhancing the electronic sensitization. Such an enhancement effect is demonstrated for various gases, including CO, CH3CH2OH, CH4, HCHO, and NH3. Additionally, the impacts of H2S treatment on the response and recovery time are also observed. Surface compositional analysis are conducted with X-ray photoelectron spectroscopy. A proposed mechanism for the enhancement effect is elaborated, highlighting the role of electronic sensitization and the sulfide-sulfate component. This research offers valuable insights into the potential applications of metal oxide sensors in sulfide-presented harsh environments in gas sensing, encouraging future exploration of optimized sensor materials, operation temperature, and the development of hydrogen sulfide poisoning-resistant and higher sensitivity MOGS.

In-Sensor Organic Electrochemical Transistor for the Multimode Neuromorphic Olfactory System.

The olfactory system is one of the six basic sensory nervous systems. Developing artificial olfactory systems is challenging due to the complexity of chemical information decoding and memory. Conventional chemical sensors can convert chemical signals into electric signals to decode gas information but they lack memory functions. Additional storage and processing units would significantly increase the complexity and power consumption of the devices, especially for portable and wearable devices. Here, an olfactory-inspired in-sensor organic electrochemical transistor (OI-OECT) is proposed, with the integrated functions of chemical information decoding, tunable memory level, and selectivity of vapor sensing. The ion-gel electrolyte endows the OI-OECT with the function of tunable memory levels and a low operating voltage. Typical synaptic behaviors, including inhibitory postsynaptic current and paired-pulse facilitations, are successfully achieved. Importantly, the gas memory level can be effectively modulated by the gate voltages (0 and -1 V), which realized the transformation of volatile and nonvolatile memory. Furthermore, benefiting from the recognition of multiple gases and ability to detect cumulative damage caused by gases, the OI-OECT is demonstrated for early warning system targeting leakage detection of two gases (NH3 and H2S). This work achieves the integrated functions of chemical gas information decode, tunable gas memory level, and selectivity of gas in a single device, which provides a promising pathway for the development of future artificial olfactory systems.