Diffusion Model Research Articles

Hierarchical differences in the encoding of amplitude modulation in the subcortical auditory system of awake nonhuman primates.

Sinusoidal amplitude modulation (SAM) is a key feature of complex sounds. Although psychophysical studies have characterized SAM perception, and neurophysiological studies in anesthetized animals report a transformation from the cochlear nucleus' (CN; brainstem) temporal code to the inferior colliculus' (IC; midbrain's) rate code, none have used awake animals or nonhuman primates to compare CN and IC's coding strategies to modulation-frequency perception. To address this, we recorded single-unit responses and compared derived neurometric measures in the CN and IC to psychometric measures of modulation frequency (MF) discrimination in macaques. IC and CN neurons often exhibited tuned responses to SAM in rate and spike-timing measures of modulation coding. Neurometric thresholds spanned a large range (2-200 Hz ΔMF). The lowest 40% of IC thresholds were less than or equal to psychometric thresholds, regardless of which code was used, whereas CN thresholds were greater than psychometric thresholds. Discrimination at 10-20 Hz could be explained by indiscriminately pooling 30 units in either structure, whereas discrimination at higher MFs was best explained by more selective pooling. This suggests that pooled CN activity was sufficient for AM discrimination. Psychometric and neurometric thresholds decreased as stimulus duration increased, but IC and CN thresholds were higher and more variable than behavior at short durations. This slower subcortical temporal integration compared with behavior was consistent with a drift diffusion model that reproduced individual differences in performance and can constrain future neurophysiological studies of temporal integration. These measures provide an account of AM perception at the neurophysiological, computational, and behavioral levels.NEW & NOTEWORTHY In everyday environments, the brain is tasked with extracting information from sound envelopes, which involves both sensory encoding and perceptual decision-making. Different neural codes for envelope representation have been characterized in midbrain and cortex, but studies of brainstem nuclei such as the cochlear nucleus (CN) have usually been conducted under anesthesia in nonprimate species. Here, we found that subcortical activity in awake monkeys and a biologically plausible perceptual decision-making model accounted for sound envelope discrimination behavior.

Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error.

The plasticisation model of dye diffusion: Part 2

AbstractTemperature dependent equilibrium dye adsorption data reported for Chlorazol Fast Scarlet 8B, Durazol Red 2B and Chrysophenine G on cotton, CV and CUP fibres and Durazol Grey RG on cotton and CV fibres in the presence of a constant amount of added inorganic electrolyte between 60°C and 90°C, as well as Chrysophenine G on cellophane sheet in the presence of varying amounts of added inorganic electrolyte, between 60°C and 97.5°C, was re‐evaluated using the Williams‐Landel‐Ferry equation. The results obtained suggest that the plasticisation model of dye diffusion appears to offer a reasonable explanation of the temperature dependent equilibrium adsorption of direct dyes on cellulosic fibres/polymers.

A Diffusion Model to Describe Water Absorption by Red Rice during Soaking: Variable Mass Diffusivity, Variable Volume, Use of Boundary-Fitted Coordinates

This article aims to carry out experiments on water absorption by husked red rice at constant temperatures of 28, 40, and 50 °C. The description of the absorption kinetics and the analyses of the water distribution and volumetric expansion of each grain, at a given instant, were carried out using a diffusion model. In order for the model to be as close as possible to the real physical situation, the mesh necessary to numerically solve the diffusion equation was generated from the photograph of a grain. Thus, the diffusion equation was written in Boundary-Fitted Coordinates (BFCs). The solution of the diffusion equation written in generalized coordinates was then discretized in space and time, using the Finite Volume method, with a fully implicit formulation, considering the variable volume and variable mass diffusivity as functions of the local moisture content. Optimizations based on the Levenberg–Marquardt algorithm make it possible to determine the parameters of an exponential function relating mass diffusivity and the local moisture content for each temperature. Statistical indicators (chi-square, determination coefficient, and Student’s t-test) allowed us to conclude that the proposed model was very satisfactory for all temperatures, making it possible to simulate the water absorption, water distribution, and volumetric expansion of the grain over time.

Identification of the coefficient in the diffusion model of hydrodynamic flow in a chemical reactor

This article analyzes in detail the actual problem of the spread of disinformation through various media resources and their impact on society. The main aspects of information consumption by Ukrainian society, in particular on the Internet, are highlighted, and the potential target audience is determined. Based on the analysis, a new intelligent system based on Natural Language Processing technology is proposed, which provides users with a comprehensive overview of the activity of each media resource. Available analogues with their advantages and limitations are examined in detail, emphasizing the significant advantages of the proposed intelligent system for increasing information literacy and countering disinformation. Refs.: 19 titles.

Adsorption studies of methylene blue on cation-modified graphene oxide and graphene oxide/MXene membranes

Membranes prepared from graphene oxide (GO) have greater advantages over traditional materials for water treatment, while MXene is a good candidate for pollutant removal. However, the effect of different cations introduced into the prepared GO membranes on their adsorption performance must be further explored. In this study, a series of cation-modified GO and GO/MXene membranes were prepared based on the self-assembly of GO. Na+-modified membranes were found to be more effective at removing methylene blue (MB) than multivalent cation-modified GO membranes. However, the cation-modified GO/MXene membranes improved the MB adsorption efficiency, and the higher the ionic valence, the more pronounced the effect. In addition, the effects of the pH, temperature, contact time and initial concentration of MB on the adsorption performance of the membranes were investigated. The best removal of MB was achieved under neutral conditions, and the adsorption process was consistent with quasi-secondary kinetics and Langmuir isotherm models. The fitting results of the Langmuir isotherm model showed that the maximum adsorption capacities of the cation-modified GO membranes for MB were 654.9, 319.8, and 246.3 mg/g, respectively. The maximum adsorption capacities of the cation-modified GO/MXene membranes for MB were 687.3, 497.6, and 772.4 mg/g, respectively. Furthermore, the results of the intraparticle diffusion model revealed that the entire adsorption process was controlled by both external and internal diffusion. The thermodynamic analysis results indicated that the entire adsorption process was a heat-absorbing process and could proceed spontaneously. That the findings suggest that the Na+-modified GO and Al3+-modified GO/MXene membranes demonstrate the best adsorption effects and are promising adsorbents for MB removal.

On discrete FitzHugh–Nagumo reaction–diffusion model: Stability and simulations

This research paper focuses on the analysis of a discrete FitzHugh–Nagumo reaction–diffusion system. We begin by discretizing the FitzHugh–Nagumo reaction–diffusion model using the second-order and L1-difference approximations. Our study examines the local stability of the equilibrium points of the system. To identify conditions that ensure the global asymptotic stability of the steady-state solution, we employ various techniques, with a primary focus on the direct Lyapunov method. Theoretical results are supported by numerical simulations that demonstrate the practical validity of the asymptotic stability conclusions. Our findings provide new insights into the stability characteristics of discrete FitzHugh–Nagumo reaction–diffusion systems and contribute to the broader understanding of such systems in mathematical biology.

Urea-modified hazelnut shell biochar (N-HSB) for efficient Cr(VI) removal: Performance and mechanism insights

Composite with a high specific surface area of 224.62 m2 g−1 was prepared by adding urea as a nitrogen source to hazelnut shell biochar (HSB). Nitrogen doping significantly enhanced the ability of biochar for Cr(VI) elimination, achieving twice the removal efficiency of unmodified biochar. The impacts of varying the pH and initial concentrations on Cr(VI) removal by urea-modified biochar (N-HSB) were investigated. The Cr(VI) removal by N-HSB was better described by intra particle diffusion model and pseudo-second order kinetic model under optimal conditions. Furthermore, XPS, FTIR, SEM, and BET analyses were used to verify the pivotal roles of oxygen- and nitrogen-containing functional groups. Electrostatic attraction, redox reaction, and complexation constituted the principal mechanisms facilitating Cr(VI) elimination by N-HSB. This study demonstrated that the modification of biochar with urea as a nitrogen source represented a promising strategy for enhancing the removal capacity of biochar for Cr(VI) in aqueous environments.

Smart fabrication of CaFe2O4 using water in-solution organic fuels via facial combustion route and its multidimensional application in dye elimination

In the present work, successive nanomagnetic-porous CaFe2O4 (CFO) fabrication was achieved using pioneer water-insoluble organic fuels in the sustainable combustion technique and examined to eliminate hazardous Acid Fuchsin (AF) dye from simulated wastewater. The synthesized CFO samples exhibit miscellaneous morphology, including rock-strewn sharp-edge cubic to porous honeycomb particles examined by FESEM analysis. The CFO shows a high surface area (36.7 to 71.2 m2/g), orthorhombic structure, and superparamagnetic nature (magnetic saturation 19.5 to 24.6 emu/g). The AF adsorption mechanism was investigated through isotherm and kinetic study, wherein the Langmuir model and the pseudo-second-order (PSO) kinetic model describe the AF adsorption. The Langmuir maximum monolayer adsorption capacity was found to be 1083 to 2505 mg/g for F1-CFO to F4-CFO (F1-F4 represent fuel 1 – fuel 4) adsorbents. However, the Intraparticle diffusion model (IPDM) shows the multi-linearity plot, suggesting that more than two mechanisms were involved in the adsorption process. The adsorption rate suggests that the morphology of the adsorbent mainly influences the adsorption process. The PSO rate constant was found to be 6.96E-05, 1.00E-04, 1.28E-04, and 1.59E-04 g/mg min-1for F1-CFO, F2-CFO, F3-CFO, and F4-CFO respectively. Innovative visible-light aided photocatalytic regeneration approach effectively generates (heterogeneous activation H2O2) exhausted adsorbents up to eight consecutive cycles: the manifold efficiency and facile magnetic isolation of CFO nanomaterial directed to the sustainable solution in environmental remediation.

Attentional dynamics of evidence accumulation explain why more numerate people make better decisions under risk

In decisions under risk, more numerate people are typically more likely to choose the option with the highest expected value (EV) than less numerate ones. Prior research indicates that this finding cannot be explained by differences in the reliance on explicit EV calculation. The current work uses the attentional Drift Diffusion Model as a unified computational framework to formalize three candidate mechanisms of pre-decisional information search and processing—namely, attention allocation, amount of deliberation, and distorted processing of value—which may differ between more and less numerate people and explain differences in decision quality. Computational modeling of an eye-tracking experiment on risky choice demonstrates that numeracy is linked to how people allocate their attention across the options, how much evidence they require before committing to a choice, and also how strongly they distort currently non-attended information during preference formation. Together, especially the latter two mechanisms largely mediate the effect of numeracy on decision quality. Overall, the current work disentangles and quantifies latent aspects of the dynamics of preference formation, explicates how their interplay may give rise to manifest differences in decision quality, and thereby provides a fully formalized, mechanistic explanation for the link between numeracy and decision quality in risky choice.

MTDiff: Visual anomaly detection with multi-scale diffusion models

Advancements in computer vision have fueled rapid developments in unsupervised anomaly detection, but current methods often encounter limitations when addressing anomalies with varying scales, and the intricate pipelines that require significant tuning efforts further hinder the usability. In this work, we propose MTDiff, a novel anomaly detection method comprising diffusion models built on different scales. In essence, the constructed scale-specific branches and their incorporation can enhance the pattern coverage, thus improving performance. MTDiff involves two parts: reconstruction that repairs the anomalous region to pseudo-normal, and detection that carefully compares and localizes the anomalies. Instead of the typical forward process of diffusion, we construct a partial Markov chain to improve the reconstruction quality. During the discrimination, we construct a simple but effective detector that operates on feature-level to better utilize the rich contextual information. MTDiff comes with a concise training pipeline, with optimized diffusion iterations ensuring the efficiency. Sufficient experiments reveal that it outperforms the state-of-the-art approaches, showing superior stability and robustness in both image- and pixel-level anomaly detection. The related code is available at https://github.com/vergilben/MTDiff.

Effective waste management through Co-pyrolysis of EFB and tire waste: Mechanistic and synergism analysis

Driven by the need for solutions to address the global issue of waste accumulation from human activities and industries, this study investigates the thermal behaviors of empty fruit bunch (EFB), tyre waste (TW), and their blends during co-pyrolysis, exploring a potential method to convert waste into useable products. The kinetics mechanism and thermodynamics properties of EFB and TW co-pyrolysis were analysed through thermogravimetric analysis (TGA). The rate of mass loss for the blend of EFB:TW at a 1:3 mass ratio shows an increase of around 20% due to synergism. However, the blend's average activation energy is higher (298.64 kJ/mol) when compared with single feedstock pyrolysis (EFB = 257.29 kJ/mol and TW = 252.92 kJ/mol). The combination of EFB:TW at a 3:1 ratio does not result in synergistic effects on mass loss. However, a lower activation energy is reported, indicating the decomposition process can be initiated at a lower energy requirement. The reaction model that best describes the pyrolysis of EFB, TW and their blends can be categorised into the diffusion and power model categories. An equal mixture of EFB and TW was the preferred combination for co-management because of the synergistic effect, which significantly impacts the co-pyrolysis process. The mass loss rate experiences an inhibitive effect at an earlier stage (320 °C), followed by a promotional impact at the later stage (380 °C). The activation energy needed for a balanced mixture is the least compared to all tested feedstocks, even lower than the pyrolysis of a single feedstock. The study revealed the potential for increasing decomposition rates using lower energy input through the co-pyrolysis of both feedstocks. These findings evidenced that co-pyrolysis is a promising waste management and valorisation pathway to deal with overwhelming waste accumulation. Future works can be conducted at a larger scale to affirm the feasibility of EFB and TW co-management.

VCC-DiffNet: Visual Conditional Control Diffusion Network for Remote Sensing Image Captioning

Pioneering remote sensing image captioning (RSIC) works use autoregressive decoding for fluent and coherent sentences but suffer from high latency and high computation costs. In contrast, non-autoregressive approaches improve inference speed by predicting multiple tokens simultaneously, though at the cost of performance due to a lack of sequential dependencies. Recently, diffusion model-based non-autoregressive decoding has shown promise in natural image captioning with iterative refinement, but its effectiveness is limited by the intrinsic characteristics of remote sensing images, which complicate robust input construction and affect the description accuracy. To overcome these challenges, we propose an innovative diffusion model for RSIC, named the Visual Conditional Control Diffusion Network (VCC-DiffNet). Specifically, we propose a Refined Multi-scale Feature Extraction (RMFE) module to extract the discernible visual context features of RSIs as input of the diffusion model-based non-autoregressive decoder to conditionally control a multi-step denoising process. Furthermore, we propose an Interactive Enhanced Decoder (IE-Decoder) utilizing dual image–description interactions to generate descriptions finely aligned with the image content. Experiments conducted on four representative RSIC datasets demonstrate that our non-autoregressive VCC-DiffNet performs comparably to, or even better than, popular autoregressive baselines in classic metrics, achieving around an 8.22× speedup in Sydney-Captions, an 11.61× speedup in UCM-Captions, a 15.20× speedup in RSICD, and an 8.13× speedup in NWPU-Captions.

Synthetic lidar point cloud generation using deep generative models for improved driving scene object recognition

The imbalanced distribution of different object categories poses a challenge for training accurate object recognition models in driving scenes. Supervised machine learning models trained on imbalanced data are biased and easily overfit the majority classes, such as vehicles and pedestrians, which appear more frequently in driving scenes. We propose a novel data augmentation approach for object recognition in lidar point cloud of driving scenes, which leverages probabilistic generative models to produce synthetic point clouds for the minority classes and complement the original imbalanced dataset. We evaluate five generative models based on different statistical principles, including Gaussian mixture model, variational autoencoder, generative adversarial network, adversarial autoencoder and the diffusion model. Experiments with a real-world autonomous driving dataset show that the synthetic point clouds generated for the minority classes by the Latent Generative Adversarial Network result in significant improvement of object recognition performance for both minority and majority classes. The codes are available at https://github.com/AAAALEX-XIANG/Synthetic-Lidar-Generation.

Elucidating the impact of non-spherical morphology on kinetic behavior of graphite using single-particle microelectrode

The precise assessment of the kinetic properties of graphite is crucial for the design of the material and the development of electrochemical models for lithium-ion batteries. However, conventional evaluation methods using composite electrodes fall short in excluding the influence of its porous structure and evaluating the influence of particle shape on kinetic behavior. In this work, we elucidate the impact of non-spherical morphology on kinetic behavior of single artificial graphite particle, employing the single-particle microelectrode technique. Three single artificial graphite particles with different sizes are pre-cycled and exhibit outstanding rate capability, maintaining a maximum capacity retention of 78.1 % at 200 C. Further, exchange current density and diffusion coefficients are determined through Tafel plots and the potentiostatic intermittent titration technique (PITT), respectively. It is found that the particle with a higher exposure of edge planes present elevated exchange current density and diffusion coefficients. Considering that the graphite particles typically feature an ellipsoidal rather than spherical shape, we propose an approach based on an ellipsoidal diffusion model for extracting diffusion coefficients. This model takes into account the non-spherical morphology of graphite, addressing the limitations inherent in the geometric assumptions of previous methods. The average relative error between the diffusion coefficients derived from the ellipsoidal diffusion model and the previous method using spherical assumptions is 215 %, indicating that accurately depicting the shape of ellipsoidal graphite particles in the model is crucial for obtaining correct estimates of kinetic parameters. This study offers direct experimental evidence of superior kinetic behavior of edge planes over basal planes. To leverage this characteristic, it is recommended to employ an out-of-plane aligned architecture for composite electrodes using novel techniques, e.g., 3D printing or magnetic alignment techniques.

Carbon isotope fractionation during methane transport through tight sedimentary rocks: Phenomena, mechanisms, characterization, and implications

The phenomenon of carbon isotopic fractionation, induced by the transport of methane in tight sedimentary rocks through processes primarily involving diffusion and adsorption/desorption, is ubiquitous in nature and plays a significant role in numerous geological and geochemical systems. Consequently, understanding the mechanisms of transport-induced carbon isotopic fractionation both theoretically and experimentally is of considerable scientific importance. However, previous experimental studies have observed carbon isotope fractionation phenomena that are entirely distinct, and even exhibit opposing characteristics. At present, there is a lack of a convincing mechanistic explanation and valid numerical model for this discrepancy. Here, we performed gas transport experiments under different gas pressures (1–5 MPa) and confining pressures (10–20 MPa). The results show that methane carbon isotope fractionation during natural gas transport through shale is controlled by its pore structure and evolves regularly with increasing effective stress. Compared with the carbon isotopic composition of the source gas, the initial effluent methane is predominantly depleted in 13C, but occasionally exhibits 13C enrichment. The carbon isotopic composition of effluent methane converges to that of the source gas as mass transport reaches a steady state. The evolution patterns of the isotope fractionation curve, transitioning from the initial non-steady state to the final steady state, can be categorized into five distinct types. The combined effect of multi-level transport channels offers the most compelling mechanistic explanation for the observed evolution patterns and their interconversion. Numerical simulation studies demonstrate that existing models, including the Rayleigh model, the diffusion model, and the coupled diffusion-adsorption/desorption model, are unable to describe the observed complex isotope fractionation behavior. In contrast, the multi-scale multi-mechanism coupled model developed herein, incorporating diffusion and adsorption/desorption across multi-level transport channels, effectively reproduces all the observed fractionation patterns and supports the mechanistic rationale for the combined effect. Finally, the potential carbon isotopic fractionation resulting from natural gas transport in/through porous media and its geological implications are discussed in several hypothetical scenarios combining numerical simulations. These findings highlight the limitations of carbon isotopic parameters for determining the origin and maturity of natural gas, and underscore their potential in identifying greenhouse gas leaks and tracing sources.

RS-Dseg: semantic segmentation of high-resolution remote sensing images based on a diffusion model component with unsupervised pretraining

Semantic segmentation plays a crucial role in interpreting remote sensing images, especially in high-resolution scenarios where finer object details, complex spatial information and texture structures exist. To address the challenge of better extracting semantic information and ad-dressing class imbalance in multiclass segmentation, we propose utilizing diffusion models for remote sensing image semantic segmentation, along with a lightweight classification module based on a spatial-channel attention mechanism. Our approach incorporates unsupervised pretrained components with a classification module to accelerate model convergence. The diffusion model component, built on the UNet architecture, effectively captures multiscale features with rich contextual and edge information from images. The lightweight classification module, which leverages spatial-channel attention, focuses more efficiently on spatial-channel regions with significant feature information. We evaluated our approach using three publicly available datasets: Postdam, GID, and Five Billion Pixels. In the test of three datasets, our method achieved the best results. On the GID dataset, the overall accuracy was 96.99%, the mean IoU was 92.17%, and the mean F1 score was 95.83%. In the training phase, our model achieved good performance after only 30 training cycles. Compared with other models, our method reduces the number of parameters, improves the training speed, and has obvious performance advantages.

Co-pyrolysis of pretreated cotton stalk and low-density polyethylene: Evolved products and pyrolysis mechanism analysis

The preparation of bio-oil from cotton stalks and agricultural residue films using co-pyrolysis technology can achieve resource recovery and energy conversion, which has important research value and significance. In this study, cotton stalks were subjected to different chemical pretreatments using NaOH, HCl, and H2O solutions to understand their structural changes and pyrolysis characteristics. In addition, the lower H/C ratio of cotton stalks resulted in higher oxygen content in the pyrolysis oil, which limited its efficient and clean utilization. Therefore, the characteristics and pyrolysis kinetics of the pyrolysis products of pretreated cotton stalks and LDPE (low-density polyethylene) were studied. The results showed that the ash content of alkali pretreatment cotton stalks decreased by 1.24 %, and the dense structure of cotton stalks significantly relaxed. NaOH pretreatment effectively removed hemicellulose sugars and cracked them. During the co-pyrolysis process, when the ratio of NaOH-CS/LDPE was 50/50, the synergistic effect was more pronounced, and the oil yield increased by 2 % compared to the theoretical value. The oxygen content of CO and CO2 in the pyrolysis gas was higher than the theoretical value, at 10.4 % and 14.1 % respectively. The synergistic effect of bio-oil on hydrocarbons was the most significant, reaching 18.9 %. More hydrogen and less oxygen migrated into the co-pyrolysis oil, resulting in an increase in hydrocarbons and a decrease in oxygen-containing compounds, and improving the quality of bio-oil. Results from electron paramagnetic resonance (EPR) indicated that adding LDPE might raise the quantity of stable free radicals. The evolution mechanism of functional groups of NaOH-CS and LDPE co-pyrolysis behavior was analyzed by Fourier in-situ infrared spectrometry (FTIR), and it was found that C–O–C, C=O, and O–H decreased due to dehydroxylation, decarboxylation, decarbonylation, and demethoxy reactions with the increase of temperature, indicating that there was a synergistic effect between NaOH-CS and LDPE co-pyrolysis. The pyrolysis kinetics of NaOH-CS, LDPE and their blends were determined by the model-free method. The introduction of LDPE can reduce the activation energy of NaOH -CS pyrolysis alone, and the 3D diffusion (D3) model is suitable for their blends.

Stabilization and sustained release of rutin dye via eco-friendly Zn/Al-LDH adsorbent: Kinetic, thermodynamic, and antioxidant investigations

This study focuses on the stabilization of rutin dye by intercalating it within Zn/Al-LDH (R-Z/A-L) through co-precipitation (CO) and ion exchange (IO) methods. For the IO method, rutin was intercalated onto Z/A-L using a circulator, achieving an optimal intercalation efficiency of 86.37 % and a capacity of 43.18 mg g−1. Comprehensive characterization of the intercalated compounds was performed using XRD, FTIR, FE-SEM, TEM, TGA, DSC, BET, and XPS techniques, confirming efficient intercalation of rutin into LDH. Kinetic analysis indicated that the pseudo-second-order and intraparticle diffusion models best illustrate the intercalation and release mechanisms, respectively. Isotherm data supported the Langmuir model as the most accurate for describing rutin's intercalation behavior. Thermodynamic investigation disclosed the process to be endothermic and spontaneous. Using the CO method, R-Z/A-L was synthesized under optimal conditions with varying rutin concentrations (5 %, 15 %, and 25 %), achieving maximum intercalation efficiencies and capacities up to 98.15 % and 49.07 mg g−1, respectively. Additionally, the antioxidant activity of the intercalated rutin was evaluated, showing that its integration into the adsorbent allowed for prolonged and consistent release. These results confirm the effectiveness of the Z/A-L adsorbent in enhancing stability.

The Bioremoval of Toxic Dyes Using Chlorella Algae in Airlift Bioreactor

<p style="text-align:justify; margin-bottom:11px"><span style="font-size:11pt"><span style="line-height:107%"><span style="font-family:Calibri,sans-serif"><span style="font-size:14.0pt"><span style="line-height:107%"><span style="font-family:"Times New Roman",serif">Malachite green (MG) and Crystal Violet (CV) are used mainly as dyestuff and antimicrobials in aquaculture. They have a severe toxic effect on the environment. Several techniques were used to remove impurities from an aqueous solution: chemical, physical, and electrical. Among all these techniques, using dry algae is a more economical and helpful process. This study aims to investigate using an airlift reactor in the removal of MG and CV by chlorella algae as a biosorbent under different variables. The experiments were carried out in an airlift bioreactor. The experiments were carried out under the effect different operating conditions of initial dye concentration (5-40 ppm), alga dosage (0.3-1.8 gm/l), pH (3–10), air flow rate (0-40 ml/min), temperature (298-318 K) and contact time (5-60 min).  The results show that the introduce of air bubbles significantly enhances the removal efficiency of the dye. The best removal effectiveness was 95.2% for MG dye and 96.1% for CV dye.  The thermodynamics results reveal that the processes are exothermic for both dyes. Kinetic and adsorption isotherms results show the best fit is pseudo-second order and Langmuir model. The mass model result shows that the liquid film diffusion model was the best-fitted for both dyes.</span></span></span></span></span></span></p>