Diffusion Model Research Articles

Modified starch as a component of environmentally friendly polymer adsorbents − from synthesis and characterization to potential application in the removal of toxic C.I. Basic Yellow 2 dye

Increasing global degradation of the aquatic environment is forcing the search for new and low-cost adsorbents of reduced toxicity, derived from natural and renewable sources. To meet these expectations, a new approach to adsorbent fabrication was developed.The study aimed to synthesize, characterize, and apply environmentally friendly adsorbents based on poly(ethylene glycol dimethacrylate-co-vinyl acetate) (EGDMA-VA) with unmodified/modified starch (St). The first step proposed a new mechanochemical method of starch modification using thiourea (T) and potassium dihydrogen phosphate (P). Then, the functionalized starch was used to obtain polymeric microspheres. The final step involves their application for the removal of toxic C.I. Basic Yellow 2 (BY2) dye. The pristine materials and adsorbents were characterized by ATR/FT-IR and XPS, DSC, SEM/EDX, porous structure, particle size distribution, tendency to swell, and pHpzc.Adsorption batch studies of BY2 as a function of time (1–60 min), initial dye concentration (1–25 mg/L, pH=8.3), temperature (298–328 K), and competing electrolyte (5–15 g/L Na2SO4) and surfactant (0.1–0.5 g/L CTAB) presence were carried out. The kinetic studies were analyzed using pseudo-first order, pseudo-second order, and intraparticle diffusion models. The equilibrium studies revealed that the Freundlich isotherm model (kF=5.62–6.56 mg1-1/n L1/n) described BY2-adsorbents systems rather than Langmuir, Temkin or Dubinin-Radushkevich. Thermodynamic parameters (ΔG°, ΔH°, and ΔS°) pointed to the exothermic nature of adsorption and its spontaneousness. Importantly, the starch-modified microspheres were regenerated, too.

Constraining Mean Landslide Occurrence Rates for Non‐Temporal Landslide Inventories Using High‐Resolution Elevation Data

AbstractConstraining landslide occurrence rates can help to generate landslide hazard models that predict the spatial and temporal occurrence of landslides. However, most landslide inventories do not include any temporal data due to the difficulties of dating landslide deposits. Here we introduce a method for estimating the mean landslide occurrence rate of deep‐seated rotational and translational slides derived solely from high‐resolution (≤3 m) elevation data and globally available estimates of the diffusion coefficient for sediment flux. The method applies a linear diffusion model to the roughest landslide deposits until they reach a representative non‐landslide roughness distribution. This estimates the time for a landslide deposit to be unrecognizable in high‐resolution digital elevation data, which we term the mean lifetime of the landslide. Using the mean lifetime and number of landslides within an area of interest, we can estimate the mean occurrence rate of landslides over that domain. We validate this approach using a comprehensive temporal inventory of landslides in western Oregon created using age‐roughness curves that are calibrated with high‐resolution elevation data and radiocarbon data. We find good agreement between our diffusion method and the existing age‐roughness‐derived estimates, producing mean lifetimes of 4500 and 5200 years (4% difference), respectively. Hazard maps produced using the two methodologies generally agree, with the maximum differences in landslide probability reaching 0.1. Due to the relative abundance of high‐resolution elevation data compared with age‐dated landslides, our method could help constrain landslide occurrence rates in areas previously considered unfeasible.

Latent-SDE: guiding stochastic differential equations in latent space for unpaired image-to-image translation

Score-based diffusion models have shown promising results in unpaired image-to-image translation (I2I). However, the existing methods only perform unpaired I2I in pixel space, which requires high computation costs. To this end, we propose guiding stochastic differential equations in latent space (Latent-SDE) that extracts domain-specific and domain-independent features of the image in the latent space to calculate the loss and guides the inference process of a pretrained SDE in the latent space for unpaired I2I. To refine the image in the latent space, we propose a latent time-travel strategy that increases the sampling timestep. Empirically, we compare Latent-SDE to the baseline of the score-based diffusion model on three widely adopted unpaired I2I tasks under two metrics. Latent-SDE achieves state-of-the-art on Cat →\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rightarrow $$\\end{document} Dog and is competitive on the other two tasks. Our code will be freely available for public use upon acceptance at https://github.com/zhangXJ147/Latent-SDE.

Agent-Based New Product Diffusion Model and Detection of Sales Decline Phenomenon with CaR

Agent-Based New Product Diffusion Model and Detection of Sales Decline Phenomenon with CaR

Generative discovery of safer chemical alternatives using diffusion modeling: A case study in green solvent design for cyclohexane/benzene extractive distillation

Over the past century, advancements in chemistry have significantly propelled human innovation, enhancing both industrial and consumer products. However, this rapid progression has resulted in chemical pollution increasingly surpassing planetary boundaries, as production and release rates have outpaced our monitoring capabilities. To catalyze more impactful efforts, this study transitions from traditional chemical assessment to inverse chemical design, introducing a generative graph latent diffusion model aimed at discovering safer alternatives. In a case study on the design of green solvents for cyclohexane/benzene extraction distillation, we constructed a design database encompassing functional, environmental hazards, and process constraints. Virtual screening of previous design dataset revealed distinct trade-off trends between these design requirements. Based on the screening outcomes, an unconstrained generative model was developed, which covered a broader chemical space and demonstrated superior capabilities for structural interpolation and extrapolation. To further optimize molecular generation towards desired properties, a multi-objective latent diffusion method was applied, yielding 19 candidate molecules. Of these, 7 were identified in PubChem as the most viable green solvent candidates, while the remaining 12 as potential novel candidates. Overall, this study effectively designed green solvent candidates for safer and more sustainable industrial production, setting a promising precedent for the development of environmentally friendly alternatives in other areas of chemical research.

Accelerating breast MRI acquisition with generative AI models.

To investigate the use of the score-based diffusion model to accelerate breast MRI reconstruction. We trained a score-based model on 9549 MRI examinations of the female breast and employed it to reconstruct undersampled MRI images with undersampling factors of 2, 5, and 20. Images were evaluated by two experienced radiologists who rated the images based on their overall quality and diagnostic value on an independent test set of 100 additional MRI examinations. The score-based model produces MRI images of high quality and diagnostic value. Both T1- and T2-weighted MRI images could be reconstructed to a high degree of accuracy. Two radiologists rated the images as almost indistinguishable from the original images (rating 4 or 5 on a scale of 5) in 100% (radiologist 1) and 99% (radiologist 2) of cases when the acceleration factor was 2. This fraction dropped to 88% and 70% for an acceleration factor of 5 and to 5% and 21% with an extreme acceleration factor of 20. Score-based models can reconstruct MRI images at high fidelity, even at comparatively high acceleration factors, but further work on a larger scale of images is needed to ensure that diagnostic quality holds. The number of MRI examinations of the breast is expected to rise with MRI screening recommended for women with dense breasts. Accelerated image acquisition methods can help in making this examination more accessible. Accelerating breast MRI reconstruction remains a significant challenge in clinical settings. Score-based diffusion models can achieve near-perfect reconstruction for moderate undersampling factors. Faster breast MRI scans with maintained image quality could revolutionize clinic workflows and patient experience.

Complete quasilinear model for the acceleration-driven lower hybrid drift instability and a computational assessment of its validity.

A complete quasilinear model is derived for the electrostatic acceleration-driven lower hybrid drift instability in a uniform two-species low-beta plasma in which current is perpendicular to the background magnetic field. The model consists of coupled nonlinear velocity space diffusion equationsfor the volume-averaged ion and electron distribution functions. Each species' diffusion coefficient depends on a time-evolving spectral density of the electric-field energy per unit volume and a time-evolving dispersion relation. The dispersion relation is expressed analytically in integral form without the use of asymptotic limits and applies to arbitrary distribution functions, so long as they can be expressed as a function of one velocity coordinate, e.g., f(v_{y}) or f(v_{⊥}). The quasilinear model conserves energy and is complete in that it fully describes the evolution of the distribution functions, including resonant and nonresonant particle-wave interactions, while accounting for distribution-function-dependent mixed-complex frequencies. The quasilinear diffusion model is solved numerically and self-consistently using a Crank-Nicolson temporal discretization and a second-order finite-volume velocity-space discretization. Numerical solutions are compared to nonlinear fourth-order accurate continuum kinetic Vlasov-Poisson simulations. Evolution of electric-field energy, growth rates, distribution functions, and diffusion coefficients are shown to be in agreement with Vlasov simulations. The quasilinear model is shown to predict anomalous transport terms, like resistivity and heating, to within a factor of order unity. Discrepancies between the quasilinear model and Vlasov simulations are assessed and attributed primarily to lack of damping in the quasilinear description and to the use of unperturbed-orbit susceptibilities in the linear theory dispersion relation. The results illuminate the predictive accuracy of the quasilinear model, place approximate bounds on its validity, and provide much needed vetting of quasilinear theory's ability to predict the nonlinear state of a microturbulent plasma.

Attribute binding enhancement improvement for diffusion model based zero-shot image segmentation

As diffusion model's potential ability to accomplish perception tasks being discovered, many researchers have tried to apply diffusion model in segmentation tasks and achieved good results. However, there are not many methods to optimize the diffusion model for segmentation tasks by improving the text side. Even if some research have pointed out that diffusion model sometimes 'misunderstand' prompt and bind attributes to wrong objects. With the development of artificial intelligence and the gradual entry of large models into people's daily lives, in addition to the performance of the model, the model's interactivity and ability to understand natural language are also more important. Based on existing zero-shot diffusion model based segmentation method, this work introduces a new method to enhance attribute binding in the embedding of prompt improve the performance of the model. Through this method, more descriptive text will get better segmentation results, which to some extent, improves the segmentation performance of the model for inputs that are more in line with natural language description habits.

Diffusion model approach to simulating electron-proton scattering events

Generative artificial intelligence is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like the Continuous Electron Beam Accelerator Facility and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our understanding of nucleons and nuclei in terms of quark and gluon degrees of freedom. The use of generative models for simulating collider events faces several challenges such as the sparsity of the data, the presence of global or eventwide constraints, and steeply falling particle distributions. In this work, we focus on the implementation of diffusion models for the simulation of electron-proton scattering events at EIC energies. Our results demonstrate that diffusion models can reproduce relevant observables such as momentum distributions and correlations of particles, momentum sum rules, and the leading electron kinematics, all of which are of particular interest in electron-proton collisions. Although the sampling process is relatively slow compared to other machine-learning architectures, we find diffusion models can generate high-quality samples. We foresee various applications of our work including inference for nuclear structure, interpretable generative machine learning, and searches of physics beyond the Standard Model. Published by the American Physical Society 2024

Computation of SWCNT/MWCNT-doped thermo-magnetic nano-blood boundary layer flow with non-Darcy, chemical reaction, viscous heating and Joule dissipation effects

CNTs have been shown to exhibit exceptional electrical and thermal conductivities, chemical and mechanical stability and physiochemical reliability in wide-ranging applications covering biomedicine, energy and aerospace. Motivated by emerging applications in nano-drug delivery in medicine and radiative ablation therapy, a steady-state mathematical model is established for magnetohydrodynamic boundary-layer transport of chemically reactive carbon nanotubes (CNTs)-doped electrically conducting blood along a porous stretching wall of a blood vessel. Multiple and single walls CNTs are considered using the model developed by Xue (2005). Heat generation, radiative flux, wall mass (concentration) slip, viscous heating and Joule magnetic dissipation are incorporated. Chemical reaction effects are addressed utilizing homogenous first-order model. The Darcy-Forchheimer drag force model is utilized for bulk matrix and inertial drag effects in the porous medium. Radiative heat-transport is studied considering Rosseland's diffusion model. The governing partial differential conservation equations for mass, momentum, energy and species are formulated in a two-dimensional Cartesian coordinate system with allied wall and free-stream boundary conditions. Via scaling transformations, a nonlinear boundary value problem is derived. Numerical computations are achieved through bvp4c scheme. The impact of selected parameters on dimensionless quantities (velocity, temperature, concentration, skin friction, local Nusselt and Sherwood numbers) are computed and elaborated through tables and graphs. A good correlation of the numerical solutions with earlier simpler models from the literature is included. The simulations show that with augmentation in Hartmann magnetic number and Forchheimer inertial drag number, significant damping in the nano-doped blood flow is produced for both SWCNTs and MWCNTs. Temperature is elevated with increment in dissipation effect i. e. Eckert number. Higher values of mass (solutal) slip parameter induce a depletion in the concentration. Nusselt number i. e. heat transfer rate to the blood vessel wall is improved with greater values of solid volume fraction for both CNTs. The present model is relevant to radiative ablation therapy combined with nano-medical treatments in blood vessels wherein constant mechanical loading via blood pressure and flow can elevate internal stresses (circumferential wall stress and endothelial shear stress) and induce morphological alterations in blood vessel wall (stretching) and endothelium in conjunction with biochemical reactions.

A computational account of conflict processing during mental imagery.

Previous studies examining conflict processing within the context of a color-word Stroop task have focused on both stimulus and response conflicts. However, it has been unclear whether conflict can emerge independently of stimulus conflict. In this study, a novel arrow-gaze mental-rotation Stroop task was introduced to explore the interplay between conflict processing and mental rotation. A modelling approach was utilized to provide a process-level account of the findings. The results of our Stroop task indicate that conflict can emerge from mental rotation in the absence of stimulus conflict. The strength of this imagery conflict effect decreases and even reverses as mental rotation angles increase. Additionally, it was observed that participants responded more quickly and with greater accuracy to small rather than large face orientations. A comparison of three conflict diffusion models-the diffusion model for conflict tasks (DMC), the dual-stage two-phase model (DSTP), and the shrinking spotlight model (SSP)-yielded consistent support for the DSTP over the DMC and SSP in the majority of instances. The DSTP account of the experimental results revealed an increased nondecision time with increasing mental rotation, a reduction in interference from incompatible stimuli, and an improved drift rate in response selection phase, which suggests enhanced cognitive control. The findings from the model-based analysis provide evidence for a novel interaction between cognitive control and mental rotation.

Analysis of axisymmetric thermoelastic diffusive half-space with variable material properties using hyperbolic two-temperature model

The aim of the current study is to investigate the variable thermal conductivity and diffusivity impact on the transient response of an axisymmetric thermodiffusive elastic half-space under axisymmetric thermal, mechanical, and concentration loads in the light of hyperbolic two-temperature generalized fractional thermoelastic diffusion model. With the help of Kirchhoff’s transform, the governing equations are converted into linear form. The resulting equations are solved by imposing the combined Laplace–Hankel transform. In converted space, the solutions for different field quantities are presented in compact form. The copper material is considered for numerical computation. By employing a mathematical inversion procedure, the solutions are converted into the original space. Numerically computed solutions for various physical quantities are illustrated in the form of graphs to show the impact of variable thermal conductivity and diffusivity, hyperbolic two temperature, and fractional parameters. Validation of the obtained results is also presented.

Debiasing with Diffusion: Probabilistic Reconstruction of Dark Matter Fields from Galaxies with CAMELS

Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter (DM) components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between DM density fields and galaxy distributions. However, this relationship can be sensitive to assumptions in cosmology and astrophysical processes embedded in galaxy formation models, which remain uncertain in many aspects. In this work, we develop a diffusion generative model to reconstruct DM fields from galaxies. The diffusion model is trained on the CAMELS simulation suite that contains thousands of state-of-the-art galaxy formation simulations with varying cosmological parameters and subgrid astrophysics. We demonstrate that the diffusion model can predict the unbiased posterior distribution of the underlying DM fields from the given stellar density fields while being able to marginalize over uncertainties in cosmological and astrophysical models. Interestingly, the model generalizes to simulation volumes ≈500 times larger than those it was trained on and across different galaxy formation models. The code for reproducing these results can be found at https://github.com/victoriaono/variational-diffusion-cdm ✎.

Xenolith Petrochronology (San Luis Potosi, Mexico) Constrains Heat Sources for Cenozoic Ultrahigh‐Temperature Metamorphism in the Lower Crust

AbstractUltrahigh‐temperature (UHT; >900°C) metamorphism drives crustal differentiation and is widely recognized in the rock record, but its geodynamic causes are debated. Previous work on granulite‐facies metapelite xenoliths from San Luis Potosí, Mexico suggests the lower crust experienced a protracted UHT metamorphic event that coincided with the onset of regional extension. To determine the duration, conditions, and heat sources of UHT metamorphism recorded by these xenoliths, this study characterizes the major‐element, trace‐element, and U‐Pb isotopic systematics of quartz, rutile, feldspar, garnet, and zircon by in situ electron microprobe (EPMA) and laser‐ablation inductively coupled‐plasma mass spectrometry (LA‐ICP‐MS), and augments these data with detailed petrography, thermobarometry, phase equilibria modeling, and diffusion modeling. Thermobarometry and phase equilibria modeling suggest peak metamorphic conditions exceeded 0.7 GPa and 900°C. Zircon petrochronology confirms >15 Myr of UHT conditions since its onset at ∼30 Ma. A small population of zircon record elevated temperatures following transition from backarc compression to extension during the waning stages of orogenesis (60–37 Ma). Garnet preserves trace‐element zoning and mineral inclusions consistent with suprasolidus garnet growth and subsequent compositional modification by intracrystalline rare‐earth element diffusion during protracted heating, with diffusion chronometry timescales in agreement with zircon data, followed by fluid‐driven remobilization of trace elements along now‐healed fractures within ∼1 Myr of eruption. In sum, these data are most compatible with lithospheric mantle attenuation or removal as the dominant heat transport mechanism driving synextensional UHT metamorphism and crustal melting, which has bearing on models for crustal differentiation and formation of modern and ancient granulite terranes globally.

Efficient valuation of variable annuities under regime-switching jump diffusion models with surrender risk and mortality risk

We present an efficient valuation approach for guaranteed minimum accumulation benefits (GMABs), guaranteed minimum death benefits (GMDBs), and surrender benefits (SBs) embedded in variable annuity (VA) contracts in a regime-switching jump diffusion model. We incorporate into the contract the risks of mortality and surrender, with these events generally monitored discretely over the life of the policy. Using a combination of the continuous-time Markov chain (CTMC) approximation and the Fourier cosine series expansion (COS) method, we determine that the valuation problem can be resolved within a regime-switching jump diffusion framework. Extensive numerical experiments showcase the efficiency of the proposed method, which proves to be more advantageous when compared to existing approaches like Monte Carlo (MC) simulation. The thorough analysis explores how model parameters affect the valuation outcomes.

Lester: Rotoscope Animation through Video Object Segmentation and Tracking

This article introduces Lester, a novel method to automatically synthesize retro-style 2D animations from videos. The method approaches the challenge mainly as an object segmentation and tracking problem. Video frames are processed with the Segment Anything Model (SAM) and the resulting masks are tracked through subsequent frames with DeAOT, a method of hierarchical propagation for semi-supervised video object segmentation. The geometry of the masks’ contours is simplified with the Douglas–Peucker algorithm. Finally, facial traits, pixelation and a basic rim light effect can be optionally added. The results show that the method exhibits an excellent temporal consistency and can correctly process videos with different poses and appearances, dynamic shots, partial shots and diverse backgrounds. The proposed method provides a more simple and deterministic approach than diffusion models based video-to-video translation pipelines, which suffer from temporal consistency problems and do not cope well with pixelated and schematic outputs. The method is also more feasible than techniques based on 3D human pose estimation, which require custom handcrafted 3D models and are very limited with respect to the type of scenes they can process.

Characterization and optimization of waste Cucurbita maxima moschata lashes for biomass composite applications

In this paper, 5 % sodium hydroxide solution, mixed 20 % acetic acid and 5 % sodium chlorite solution, and 5 % silane coupling agent solution were successively used to soak the waste pumpkin (Cucurbita maxima moschata) lashes. Subsequently, FTIR, XRD, thermogravimetric analysis and other experimental methods were used to comprehensively characterize the properties of the discarded pumpkin lashes. On this basis, this work prepared cement mortar specimens with unadulterated pumpkin lashes, adulterated raw pumpkin lashes, and adulterated modified pumpkin lashes, and their residual mechanical properties were investigated after exposure to elevated temperatures. The experimental results indicate that the chemical modification utilized in this paper can increase the cellulose content of pumpkin lashes as well as reduce the proportion of amorphous components, and thus, improve the tensile strength, crystallinity index, grain size, thermal stability, and surface roughness of the samples. Meanwhile, the chemical treatment process can effectively reduce the water absorption and density of pumpkin lashes. The calculation outcomes of our thermal kinetics study showed that the apparent activation energies corresponding to the main pyrolysis stage of the treated pumpkin lashes were significantly enhanced. The one-dimensional diffusion model and the second-order model could better characterize the pyrolytic mechanism of the raw pumpkin lashes, while the mechanism was closer to the third-order model because of the chemical modification. Finally, the addition of chemically modified pumpkin lashes reduced the mass loss rate of cement mortar specimens at elevated temperatures while clearly increasing the residual strength of the specimens at 300 and 400 °C compared to those with referential pumpkin lashes.

FHS-adapter: fine-grained hierarchical semantic adapter for Chinese landscape paintings generation

How to migrate text-to-image models based on pre-trained diffusion models to adapt them to domain generation tasks is a common problem. In particular, the generation task for Chinese landscape paintings with unique characteristics suffers from a scarcity of fine-grained contextual details specific to such artwork. Moreover, the use of substantial amounts of non-landscape painting data during pre-training predisposes the model to be swayed by alternative visual styles, thereby leading to generated images that inadvertently lack the distinctive traits inherent to Chinese paintings. In this paper, we propose a Fine-grained Hierarchical Semantic Adapter for Chinese landscape paintings generation, namely FHS-adapter. The method orchestrates the diffusion process in a batch-wise manner, leveraging external fine-grained multi-perspective information to guide it. It gradually diminishes the influence of other style images embedded in the pre-trained diffusion model, ultimately preserving a greater number of landscape painting elements. The encoder was also replaced with the Taiyi-CLIP encoder, which is adapted for Chinese. We propose T2ICLP, a multimodal dataset containing 10,000 high-quality image-text pairs of Chinese landscape paintings. Unlike previous datasets, this dataset extracts fine-grained textual information from four perspectives, including Meta, Description, Sentiment, Poem. We compared the proposed model with the mainstream diffusion-based T2I models. Through an anonymous user study, our FHS-adapter method performs well in simulating various aspects such as brushwork, e.g.‘Gou, Cun, Dian, Ran’ means hooking, texturing, dotting, and dyeing, compositional space, elemental proportions, and color usage of different painting genres and artists. Our dataset is available at https://github.com/T2ICLP/t2iclp.

TransDiffSeg: Transformer-Based Conditional Diffusion Segmentation Model for Abdominal Multi-Objective.

In the domain of medical image segmentation, traditional diffusion probabilistic models are hindered by local inductive biases stemming from convolutional operations, constraining their ability to model long-term dependencies and leading to inaccurate mask generation. Conversely, Transformer offers a remedy by obviating the local inductive biases inherent in convolutional operations, thereby enhancing segmentation precision. Currently, the integration of Transformer and convolution operations mainly occurs in two forms: nesting and stacking. However, both methods address the bias elimination at a relatively large granularity, failing to fully leverage the advantages of both approaches. To address this, this paper proposes a conditional diffusion segmentation model named TransDiffSeg, which combines Transformer with convolution operations from traditional diffusion models in a parallel manner. This approach eliminates the accumulated local inductive bias of convolution operations at a finer granularity within each layer. Additionally, an adaptive feature fusion block is employed to merge conditional semantic features and noise features, enhancing global semantic information and reducing the Transformer's sensitivity to noise features. To validate the impact of granularity in bias elimination on performance and the impact of Transformer in alleviating the accumulated local inductive biases of convolutional operations in diffusion probabilistic models, experiments are conducted on the AMOS22 dataset and BTCV dataset. Experimental results demonstrate that eliminating local inductive bias at a finer granularity significantly improves the segmentation performance of diffusion probabilistic models. Furthermore, the results confirm that the finer the granularity of bias elimination, the better the segmentation performance.

Removal of two cationic dyes by adsorption on F400 commercial activated carbon

In this investigation, we studied the removal of two cationic dyes, methyl green MG and malachite green Mc G, by a commercial adsorbent. This adsorbent is a F400 activated carbon with a specific surface area of 1203 m2. g-1, which is expected to be high compared with ordinary adsorbents. Adsorption tests were carried out on both dyes in batch mode. The results show that both kinetics are fast, with equilibrium reached after 20 minutes and 30 minutes respectively for Methyl Green and Malachite Green. The kinetics of both dyes are well adapted to pseudo-second order, and for the intraparticle diffusion model, two lines can be distinguished for each dye, proving the existence of two adsorption steps. The isotherm for this system reveals an L-type shape for both dyes, in line with the classification of Giles & al. so there is little competition for sorption sites between solvent and adsorbate. The adsorption isotherms for methyl green by PAC obeyed the Langmuir, Freundlich and Temkin models, while that for malachite green followed only the Langmuir isotherm, with a much higher maximum amount.