Dynamic Analysis Techniques Research Articles

Nonlinear dynamical systems to study epileptic seizures and extract average amount of mutual information from encephalographs – Part I

In this study, we apply the non-linear dynamical systems theory for the assessment built on recurrence-quantification analysis technique for characterizing—differentiating non-linear electro encephalograph (EEG) signals dynamics. The technique offers convenient quantifiable data plus information over normal, tumultuous, or probability and statistical stochastic properties of inherent systems dynamics theory. The R.Q.A-established processes as the quantifiable mathematical features of non-linear electroencephalograph signals dynamics. Average amount of mutual information (AAMI) applied to compute highly applicable feature-manifestation sub-sets out of R.Q.A-built centered-features. The chosen features were then fed into the computer using artificial intelligence based neural net-works for clustering the data of encephalograph-signals to identify ictic(i.e.,ictal), inter ictal, followed by state of controls. The study is implemented by validating R.Q.A with a data base for various issues of categorization. Results showed that the combination of five selected features created on AAMI attained the precision of100% and proves dominance of R.Q.A. Nonlinear dynamical control systems theory and analysis techniques centered on R.Q.A can be used as an appropriate methodology for distinguishing the non-linear systems dynamics of encephalograph signals data also epileptic seizures tracing.

Developed drift damage index-based failure criterion for framed-wall structure system

Recently, significant progress has been observed in high-rise buildings, positively affecting the economy and society. Since the damage and failure of these structures could cause tremendous disasters for society and humanity, investigating the damage performance of the building and assessing this damage is urgently needed. The numerical methods combined with the damage mechanics theory have shown to be a crucial tool to simulate and capture the damage of these structures. Therefore, this study introduces an effective simulation procedure and performs the non-linear dynamic analysis of a framed-wall structure with different heights under various seismic events and intensities using the incremental dynamic analysis technique. The tensile damage is well captured through the adopted damage model, and it is illustrated that the damage initiation and propagation are varied at different seismic events, which significantly affects the structure response. Moreover, an extensive study on the damage assessment has been provided to evaluate the damage degree of the structure using the Park-Ang damage index. The key point of this study is to recognize an efficient criterion to determine the structure’s state under different dynamic loads. Accordingly, a new formula has been proposed to quantify the damage to the framed-wall structures efficiently with low computational efforts, which will be of great importance in the case of assessing the damage for the city-scale model rapidly. This study demonstrated that the proposed formula could accurately assess the damage induced in the framed-wall structures where the results agree with the calculated values through the Park-Ang damage index. Finally, the results of the collapse state have been emphasized through the collapse analysis, which proves the efficiency and reliability of the proposed formula.

Performance assessment of a distribution system by simultaneous optimal positioning of electric vehicle charging stations and distributed generators

Extensive penetration of Renewable Energy based Distributed Generator (REDG) units in a Distribution System necessitates its efficient and optimal applicability. Plug-in Electric Vehicles (PEV) is currently gaining importance steadily in a distribution system due to its environmental compatibility. However, suitable positioning of PEV is a mandate as it increases the overall demand of the system and has appreciable impact on the system's technical performance in terms of losses, voltage profile, penetration level, to name a few. This paper proposes a simple apparent power loss driven technique of Electric Vehicle Charging Station (EVCS) placement along with a REDG unit in a distribution system taking time-driven load models into consideration. The initial State of Charge of the PEV is reformulated taking traffic and weather conditions, proportional distance covered by the electric vehicle and non-availability of EVCS into consideration using Dynamic Fault Tree Analysis and Bayesian optimization techniques. An optimum combination of different sets of PEV with proportional distance coverage is considered. Results are validated on the basis of computation of three REDG assessment indices in comparison with an arbitrary slot of positioning of the above elements.

Numerical Technique for Implementation of SDBD Plasma Actuators for Flow Control Applications in Wing Surfaces

In the subsequent study a Computational Fluid Dynamics (CFD) analysis technique to simulatea plasma actuator over an airfoil Re= O(205) is presented. The technique uses a two-dimensional Reynolds-Averaged Navier-Stokes Method coupled to the Kloker plasma-fluid model to study the effects of a Single Dielectric-Barrier Discharge (SDBD) as a Plasma Actuator. The CFD technique have been implemented in OpenFOAM platform for two setups, when: i) the actuator was located at x/c=0.03 and ii) x/c=0.1 of the chord length of the airfoil. The magnitude of the body force is equivalent to the results obtained by Hofkens and the actuator operates for both cases in the continuous and in the burst mode. To perform the numerical technique for a stable solution in OpenFOAM,various numerical procedures were tested, including a mixed solver between PISO and SIMPLE algorithm better known as pimpleFoam with nCorrector. The cases were solved in parallel on distributed processors using OpenMPI implementation and the accuracy of the resultsare strongly depends on the choice of grid size, y-plus,wall function and discretization scheme. The results indicate a high potential, suitability and great capabilities of this numerical technique implemented in OpenFOAM platform for free instability flow simulation.

A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client-server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video.

ERACE: Toward Facilitating Exploit Generation for Kernel Race Vulnerabilities

Since a large number of Linux kernel vulnerabilities are discovered every year, many vulnerabilities cannot be patched in time. Security vendors often prioritize patching high-risk vulnerabilities, and the ratings of vulnerabilities need to be evaluated based on factors such as exploitability and the scope of influence. However, evaluating exploitability is challenging and time-consuming, especially for race vulnerabilities, whose exploitation process is complicated and the success rate of exploitation is low, making them more likely to be overlooked. In this paper, we propose a new framework, called ERACE, to facilitate the process of exploiting kernel race vulnerabilities. Given a program called a proof of concept (PoC) that can trigger a race vulnerability, ERACE first applies a combination of dynamic and static analysis techniques to locate the instruction that causes the race. Then, it applies code instrumentation and static analysis to determine the timing relationship between the race instructions and the triggering type of the vulnerability and records the vulnerability context information. Next, it uses backward taint analysis to identify checkpoints that can be used to determine whether the race condition and heap spraying are satisfied and records the system calls to which the checkpoints belong. Finally, we can generate exploits based on the information collected above. To demonstrate the utility of ERACE, we tested it on 23 real-world vulnerabilities. As a result, we successfully detected the race points of 19 vulnerabilities, the timing relationship among the race instructions, and the triggering types of 17 vulnerabilities and succeeded in generating exploits for 13 vulnerabilities. ERACE can effectively help security researchers simplify the analysis process of kernel race vulnerabilities, select appropriate exploitation methods, and use checkpoints to increase the success rates of exploitations.

R&D Performance Evaluation in the Chinese Food Manufacturing Industry Based on Dynamic DEA in the COVID-19 Era

Nowadays, China’s food consumption structure is shifting from being survival-oriented to health-oriented. However, the food industry is still facing a research and development (R&D) dilemma. Scientific evaluation of an enterprise’s R&D performance can help to reduce the investment risk of R&D and promote economic benefits. This study implements the dynamic data envelopment analysis (DDEA) technique to measure and evaluate the level of R&D performance in the Chinese food manufacturing industry. Twenty-eight listed companies were selected for the study, considering the time period from 2019 to 2021. After constructing a system of inputs, outputs and carry-over indicators, overall and period efficiency scores were obtained. The results reveal that the overall level of R&D in the industry is relatively low (0.332). Average efficiency scores across years were estimated as 0.447, 0.460, 0.430 for 2019, 2020, and 2021, respectively. Lastly, this study considers the actual business situation of the industry and makes suggestions for improvement from the perspective of enterprises and the government; these anticipate aiding the food manufacturing industry to improve the performance management of R&D activities.

Model of Execution Trace Obfuscation Between Threads

Advanced reverse analysis tools have significantly improved the ability of attackers to crack software via dynamic analysis techniques, such as symbol execution and taint analysis. These techniques are widely used in malicious fields such as vulnerability exploitation or theft of intellectual property. In this paper, we present an obfuscation strategy called “execution trace obfuscation,” wherein the program execution trace repeatedly switches between multiple threads. Our technique realizes equivalent code transformation by abstracting the obfuscation problems into pruning, cloning, and coloring problems in graph theory. Based on this, we further propose the cascade encryption of a function that depends on execution trace information with a key derived from the function address calculation process, followed by removing this key from the program. We have implemented a compiler-level system that inputs a source program and automatically generates an obfuscated file. Finally, random test proves the universality of obfuscation algorithm and verify the system’s performance. Results shows that our system can effectively interfere advanced reverse analysis tools.

Numerical collapse simulation based critical state analysis for framed‐wall structure subjected to earthquakes

SummaryWith the rapid developments of the constitutive theory, numerical methods, and computer software and hardware, full attention has been paid to establishing nonlinear structural models and investigating the nonlinear behavior, damage performance, and failure criterion of the structures. Consequently, this study introduces a precise analysis with a highly accurate level of simulation for an existing framed‐wall structure excited with strong ground motions. The incremental dynamic analysis (IDA) technique has been adopted to investigate the nonlinear behavior of the structure. The damage evolution and collapse pattern have been well captured in the structure based on the adopted damage model. The results showed that the maximum interstory drift (ISD) position varies at different seismic loads due to the damage initiation and propagation variation. The IDA curves show the end of the elastic stage at 0.70, 0.50, 0.70, and 0.20 g at San Francisco, Italy, Northridge, and San Fernando ground motions, respectively. Also, different collapse patterns of the structure have been observed at different ground motions and also at different intensities for the same ground motion. Therefore, the high‐rise building design should account for multiple collapse patterns. Moreover, a new IDA‐based technique is proposed to estimate the structure's maximum seismic capacity (MSC) and ensure it through the collapse analysis. The structure adopted in this study reaches its MSC at the San Francisco event at an intensity of 2.50 g. However, the MSC decreased by 40% at Italy and Northridge events and 60% at the San Fernando event. Since the seismic capacity of the structure is considered an essential ingredient in the design process, therefore, the findings of this study are supposed to lay the basis for the performance‐based seismic design of the structure.

Tracking the transient: Real-time exploration of electromaterials by dynamic single energy X-ray spectroelectrochemistry

Electrochemical characterization of an electromaterial normally combines steady-state and dynamic analysis techniques to identify and parameterize key charge–transfer processes along with coupled material transformations and chemical/catalytic reactions. On the other hand, highly informative complementary characterization methods, such as in situ X-ray spectroscopy, are most commonly applied in a potentiostatic mode, probing only quasi-stabilized states of the material. While being of immense importance to the field, such approaches provide very limited information on dynamic processes and their kinetics. This limitation can be resolved by combining the steady-state analysis with Dynamic Single Energy X-ray Spectroelectrochemistry – a technique of focus herein. The manuscript outlines the basic principles of this method, identifies the scope of its applications, and provides an overview of its progressively increasing use in in situ / operando research of electromaterials. • Dynamic spectroelectrochemistry is highly complementary with steady-state analysis. • Dynamic in situ XAS probes redox transformation kinetics in element-selective fashion. • Surface sensitivity and time resolution of the method can be increased in the future.

Efficient recognition of dynamic user emotions based on deep neural networks

The key issue at this stage is how to mine the large amount of valuable user sentiment information from the massive amount of web text and create a suitable dynamic user text sentiment analysis technique. Hence, this study offers a writing feature abstraction process based on ON-LSTM and attention mechanism to address the problem that syntactic information is ignored in emotional text feature extraction. The study found that the Att-ON-LSTM improved the micro-average F1 value by 2.27% and the macro-average F value by 1.7% compared to the Bi-LSTM model with the added attentivity mechanisms. It is demonstrated that it can perform better extraction of semantic information and hierarchical structure information in emotional text and obtain more comprehensive emotional text features. In addition, the ON-LSTM-LS, a sentiment analysis model based on ON-LSTM and tag semantics, is planned to address the problem that tag semantics is ignored in the process of text sentiment analysis. The experimental consequences exposed that the accuracy of the ON-LSTM and labeled semantic sentiment analysis model on the test set is improved by 0.78% with the addition of labeled word directions compared to the model Att-ON-LSTM without the addition of labeled semantic information. The macro-averaged F1 value improved by 1.04%, which indicates that the sentiment analysis process based on ON-LSTM and tag semantics can effectively perform the text sentiment analysis task and improve the sentiment classification effect to some extent. In conclusion, deep learning models for dynamic user sentiment analysis possess high application capabilities.

Strain prediction and benchmark with measurement in the Spallation Neutron Source mercury target without gas injection

The 1.4-MW Spallation Neutron Source (SNS) currently operates as the world's highest time-average power spallation source and uses mercury as the spallation target material. The mercury circulates through a stainless-steel target vessel and heat exchanger loop that removes the heat deposited from the incident proton beam in the target. The mercury vessel is part of the fixed but replaceable target module. The target module is not safety credited in the SNS design, but reliable long life is vital for meeting operational goals and minimizing cost and waste. SNS is a short-pulse ($\ensuremath{\sim}0.7\text{ }\text{ }\ensuremath{\mu}\mathrm{s}$) neutron source with a repetition rate of 60 Hz with 1.0 GeV protons onto the target. The short-pulse operation introduces two mechanisms that threaten the longevity of the mercury vessel. The pulsed target heating is essentially isochoric, which results in a distributed pressure field in the mercury with each pulse. After each pulse, the pressure propagates, interacts with, and strains the stainless-steel vessel. For intermittent times, this pressure field leads to mercury cavitation. The first mechanism of concern for the target lifetime is high-cycle fatigue to the stainless-steel vessel and the second is cavitation erosion. At the onset of the SNS project, the R program sought to address the need for an accurate and practical method to estimate the fatigue life of the mercury vessel. Around 2000, the R team conducted a series of in-beam mercury target experiments at the Weapons Neutron Research Facility at Los Alamos Neutron Science Center. These experiments successfully produced a small database of pulse response strain data. The data were used to develop and benchmark an explicit dynamic finite element analysis technique for estimating target fatigue life. SNS is now a mature facility with about 15 years of operation and 29 SNS target modules consumed. Since 2015, strain measurements have been collected from actual targets, and abundant target vessel strain data are now available in greater detail than was possible in 2000. Variances in pulse response data have been characterized using tolerance interval analysis to benchmark the simulation technique more thoroughly. The simulated results were found to match measurements generally well, but the method is always conservative. A potential adjustment to the cavitation threshold parameter value was suggested. Variances in the pulse response data are moderate, which is largely explained by the fact that data were acquired in 60-Hz burst mode and by the lasting vibration of the vessel between pulses.

Measurement of sediment particle size distribution and shape using dynamic imaging analysis

Hydropower plants operating in fragile mountainous regions face severe sediment erosion of hydraulic turbines, resulting in decreased efficiency, frequent power interruptions, and downtime for maintenance. The sediment size and shape are detrimental to the hydraulic turbines and influence sediment erosion. Sediment management is a crucial aspect of tackling sediment erosion in hydraulic turbines. The traditional method of measuring particle size and shape is time-consuming, labour-intensive, and usually requires statistical analysis. The present study applies the recent technique of dynamic imaging analysis to determine the particle size distribution and shape of sediment. Five different silt-sand mixtures have been used to check the ability of dynamic imaging analysis. Further, the repeatability and reliability of the results were checked by carrying out five repeated measurements of each sample under the same condition in a short period of time. This study will help the researchers and sedimentologists to adopt the dynamic imaging analysis technique for measuring particle size and shape and help the instrument developer in designing a better future version of the instrument.

Studies of curing cycle of carbon fiber/epoxy resins (8552� and M21�) prepregs based on thermal and rheological analyses

Thermal and rheological characterizations were performed on prepreg produced with two different commercial epoxy resins - M21� and 8552� � aiming to study and optimize the curing cycle of structural components used in aerospace industry. Characterizations were performed by differential scanning calorimetry (DSC), rheology and dynamic mechanical analysis techniques were assessed and the results were correlated and supported by Fourier Transform Infrared spectroscopy. Additionally, to heating rates suggested by the material supplier, DSC analysis allowed to evaluate further heating rates: 2, 5, 10, 15 and 20 �C/min. Materials presented the n fractional order kinetic of cure and have in its formulation the presence of thermoplastics, in addition to epoxy and amine. Results confirmed that the best heating rates for processing both materials are the lower ones, as they result in a better control of the reactions between chemical compounds involved and the physical changes that are part of curing process stages. Results have analytically confirmed the suggested proposal for curing cycle from supplier is the best choice for materials involved.

Indium-gallium-zinc oxide Schottky diodes on softening substrates for rectifying bioelectronic circuits

Incorporating electronic components onto soft materials facilitates the development of compliant electronics suited for bioelectronic applications. In this work, we present indium-gallium-zinc-oxide (IGZO) Schottky diodes fabricated on a stimuli-responsive polymer that undergoes softening (i.e. orders-of-magnitude drop in modulus) upon exposure to physiological stimuli. These diodes rectify megahertz radio-frequency (RF) signals in half-wave rectification circuits across the softening of the polymer substrate and withstand mechanical and chemical stresses such as repeated folding up to 10 000 cycles and aging in a simulated physiological medium for up to two weeks. The effects of thermal annealing and ultraviolet-ozone treatment processes are evaluated using dynamic mechanical analysis and x-ray photoelectron spectroscopy techniques, showing that these processes lead to a large improvement in the interface properties of the platinum-IGZO Schottky contact while preserving the thermomechanical properties of the softening polymer substrate. The RF rectification capabilities of these diodes in softened and deformed states are particularly interesting for the next generation of soft wireless bioelectronics.

Numerical Analysis Method for Liquefaction-induced Damage Evaluation to Railway Structure

This study aimed to suggest a method for assessing the liquefaction-induced damage to railway embankments, one of the major national SOC infrastructures. To achieve that purpose, the railroad design and maintenance standards in South Korea were examined, and risk assessment method was proposed. The risk assessment of liquefaction-induced damage was classified into no damage level, caution level, and critical level based on the assessment of vertical irregularity, residual settlement, and lateral displacement. To assess the liquefaction-induced damage, three simplified assessment methods and a dynamic numerical analysis method with a finite difference method were proposed. Finally, these methods were applied to the roadbed of the Honam high-speed line, and its applicability was investigated. According to the results, the related standards showed the most overestimated value of settlement among the simplified assessment methods, and the dynamic numerical analysis technique showed the most underestimated value of settlement.

A Feedback System Supporting Students Approaching a High-Level Programming Course

This study analyzes the mistakes students are prone to make in programming and uses the GDB and Valgrind tools to implement dynamic analysis techniques for their eventual application to programs created by students. In the analysis process, spectral error localization technology is used to strengthen the dynamic analysis to find errors more accurately. The analyzed results are sorted and corresponding feedback is given to students in order for them to better understand the content of errors when revising the program and classifying and counting the types of errors made. This study sorts mistakes frequently made by students and topics in which students are likely to make certain mistakes. The developed system was implemented in experiments including students from a programming course who were divided into two groups, namely the experimental group and the control group. A system for both groups of students to upload and submit assignments and a code analysis and feedback improvement system were used. Students in the control group only used the assignment uploading and submitting system for basic assignment uploading, verification, and the comparison of test data. After the program was entered, declarative sentence disassembly and dynamic slicing were suggested. Data were sent to GNU Debugger (GDB) and Valgrind for spectral error location; the classification and recording of error types; and the interpretation of the number of error lines, error types, and related variables. Feedback and a generated report were sent back to the student interface to provide effective and useful feedback to the students in the experimental group for them to revise their homework and record the types and number of errors they made in that week’s homework in the database. The answers provided by the students to the questions were recorded. The analysis of the pass rates of the students in the experimental and control groups for each homework test aided the understanding of the differences in the learning success of the two groups of students each week. The weekly pass rates and the numbers of measured errors in the experimental group compared with in the control group were input into a distribution map to allow us to better understand whether there was any positive correlation between the detected information, feedback to the students, pass rates of the tests, and other related data. The system statistically obtained feedback and the degree of improvement of homework programs; then, it distributed specially designed questionnaires to all students to directly obtain and quantify their feedback and perceived benefits of this system, thereby verifying the effectiveness of the system and its practicality.

Identification of errors-in-variables ARX models using modified dynamic iterative PCA

Identification of autoregressive models with exogenous input (ARX) is a classical problem in system identification. This article considers the errors-in-variables (EIV) ARX model identification problem, where input measurements are also corrupted with noise. The recently proposed Dynamic Iterative Principal Components Analysis (DIPCA) technique solves the EIV identification problem but is only applicable to white measurement errors. We propose a novel identification algorithm based on a modified DIPCA approach for identifying the EIV-ARX model for single-input, single-output (SISO) systems where the output measurements are corrupted with coloured noise consistent with the ARX model. Most of the existing methods assume important parameters like input-output orders, delay, or noise-variances to be known. This work’s novelty lies in the joint estimation of error variances, process order, delay, and model parameters. The central idea used to obtain all these parameters in a theoretically rigorous manner is based on transforming the lagged measurements using the appropriate error covariance matrix, which is obtained using estimated error variances and model parameters. Simulation studies on two systems are presented to demonstrate the efficacy of the proposed algorithm.

A Novel Technique for Dynamic Analysis of an Electromagnetic Rail Launcher using FEM Coupled with Simplorer

The performance of a rail gun depends on the current density distribution over the rail and armature as it determines the force that accelerates the projectile of the rail gun. A finite element method (FEM) coupled with Simplorer was developed to model and study the performance of the rail gun. The rail gun was modeled using an ANSYS eddy current field solver to determine the current density distribution and equivalent rail gun circuit for the given rail gun geometry. The armature velocity was then calculated using Simplorer by coupling the obtained equivalent rail gun circuit and exciting the rails using a capacitor-based pulsed power supply (PPS) system. The FEM coupled with Simplorer method was verified by numerical calculations for the rectangular rails and also with other researchers’ value, and that showed a good agreement between the results. Further, the current density distribution over rails and armature and velocity of the armature was calculated for different rail cross sections such as circular concave, circular convex, rectangular concave, rectangular convex, T-shaped concave, and T-shaped convex with a C-shaped armature. It was observed that the circular convex rail gun with C-shaped armature showed minimum current density distribution and gives a higher value of armature velocity compared with other rail gun structures. Thus, the circular convex armature was found to be suitable for the electromagnetic (EM) rail gun launchingsystem.

Development of in situ polymerized amines into mesoporous silica for direct air CO2 capture

Direct air CO2 capture (DAC) is inevitable to achieve negative emissions and lower CO2 concentration in the atmosphere. The use of class III supported amine materials has shown great potential as effective materials for CO2 capture from both flue gas and low concentrations of CO2. In this work, a linear polyethylene amine tethered to mesoporous silica foam was synthesized by controlled in situ cationic ring opening polymerization of 2-methyl-2-oxazoline. The sorbent was characterized based on porosity, FTIR, elemental analysis, and surface morphology. The CO2 capture performance at different temperatures was measured using a volumetric method, whereas the dynamic breakthrough analysis (DBA) technique was evaluated using simulated air of 400 ppm CO2 at different temperatures and relative humidities. The CO2 adsorption isotherms were studied in the temperature range from 5 to 80 °C and were fitted using different isotherm models. To evaluate the effects of moisture on the CO2 capture performance, breakthrough experiments were performed using 400 ppm CO2 at various relative humidities (0–65%). It was found that the CO2 uptake calculated from the breakthrough experiment is enhanced at higher moisture contents and decreases at higher temperatures. The maximum CO2 uptake was 1.50 mmol/g at 25 °C and 65% RH. The CO2 adsorption kinetics of the sorbent was found to follow the Avrami model, whereas the dual site Langmuir model was found to be the best fit for the adsorption isotherms. The developed sorbent exhibited a stable cyclic performance and retained its initial CO2 uptake during 10 consecutive cycles. This study demonstrates that in situ polymerization of amines into porous supports is a viable route for designing sorbents with high CO2 adsorption performance for DAC applications.