Selective Tuning Research Articles

Multifunctional diamond‐based catalysts: Promising candidates for energy conversions in extreme environments—A mini‐review

AbstractIn order to properly utilize the abundant CO2 and water resources, various catalytic materials have been developed to convert them into valuable chemicals as renewable fuels electrochemically or photochemically. Currently, most studies are conducted under mild laboratory conditions, but for some extreme environments, such as Mars and space stations, there is an urgent need to develop new catalysts satisfying such special requirements. Conventional catalytic materials mainly focus on metals and narrow bandgap semiconductor materials, while the research on wide and ultrawide bandgap materials that can inherently withstand extreme conditions has not received enough attention. Given the robust stability and excellent physico‐chemical properties of diamond, it can be expected to perform in harsh environments for electrocatalysis or photocatalysis that has not been investigated thoroughly. Here, this review summarizes the catalytic functionality of diamond‐based electrodes with various but tunable product selectivity to obtain the varied C1 or C2+ products, and discusses some important factors playing a key role in manipulating the catalytic activity. Moreover, the unique solvation electron effect of diamond gives it a significant advantage in photocatalytic conversions which is also summarized in this mini‐review. In the end, prospects are made for the application of diamond‐based catalysts under various extreme conditions. The challenges that may be faced in practical applications are also summarized and future breakthrough directions are proposed at the end.

ProteoCure: A European network to fine-tune the proteome

Proteins are essential molecular actors in every cellular process. From their synthesis to their degradation, they are subject to continuous quality control mechanisms to ensure that they fulfil cellular needs in proper and timely fashion.Proteostasis is a key process allowing cells or organisms to maintain an appropriate but dynamic equilibrium of their proteome (the ensemble of all their proteins). It relies on multiple mechanisms that together control the level, fate and function of individual proteins, and ensure elimination of abnormal ones. The proteostasis network is essential for development and adaptation to environmental changes or challenges. Its dysfunctions can lead to accumulation of deleterious proteins or, conversely, to excessive degradation of beneficial ones, and are implicated in many diseases such as cancers, neurodegeneration, or developmental and aging disorders. Manipulating this network to control abundance of selected target proteins is therefore a strategy with enormous therapeutic or biotechnological potential.The ProteoCure COST Action gathers more than 350 researchers and their teams (31 countries represented) from the academic, clinical, and industrial sectors, who share the conviction that our understanding of proteostasis is mature enough to develop novel and highly specific therapies based on selective tuning of protein levels. Towards this objective, the Action organizes community-building activities to foster synergies among its participants and reinforce training of the next generation of European researchers. Its ambition is to function as a knowledge-based network and a creative exchange hub on normal and pathologic proteostasis, focusing on developing innovative tools modulating the level of specific protein(s).

Predicting Tumor Dynamics Post-Staged GKRS: Machine Learning Models in Brain Metastases Prognosis.

This study assesses the predictive performance of six machine learning models and a 1D Convolutional Neural Network (CNN) in forecasting tumor dynamics within three months following Gamma Knife radiosurgery (GKRS) in 77 brain metastasis (BM) patients. The analysis meticulously evaluates each model before and after hyperparameter tuning, utilizing accuracy, AUC, and other metrics derived from confusion matrices. The CNN model showcased notable performance with an accuracy of 98% and an AUC of 0.97, effectively complementing the broader model analysis. Initial findings highlighted that XGBoost significantly outperformed other models with an accuracy of 0.95 and an AUC of 0.95 before tuning. Post-tuning, the Support Vector Machine (SVM) demonstrated the most substantial improvement, achieving an accuracy of 0.98 and an AUC of 0.98. Conversely, XGBoost showed a decline in performance after tuning, indicating potential overfitting. The study also explores feature importance across models, noting that features like "control at one year", "age of the patient", and "beam-on time for volume V1 treated" were consistently influential across various models, albeit their impacts were interpreted differently depending on the model's underlying mechanics. This comprehensive evaluation not only underscores the importance of model selection and hyperparameter tuning but also highlights the practical implications in medical diagnostic scenarios, where the accuracy of positive predictions can be crucial. Our research explores the effects of staged Gamma Knife radiosurgery (GKRS) on larger tumors, revealing no significant outcome differences across protocols. It uniquely considers the impact of beam-on time and fraction intervals on treatment efficacy. However, the investigation is limited by a small patient cohort and data from a single institution, suggesting the need for future multicenter research.

Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches

PurposeLiver disease causes two million deaths annually, accounting for 4% of all deaths globally. Prediction or early detection of the disease via machine learning algorithms on large clinical data have become promising and potentially powerful, but such methods often have some limitations due to the complexity of the data. In this regard, ensemble learning has shown promising results. There is an urgent need to evaluate different algorithms and then suggest a robust ensemble algorithm in liver disease prediction.MethodThree ensemble approaches with nine algorithms are evaluated on a large dataset of liver patients comprising 30,691 samples with 11 features. Various preprocessing procedures are utilized to feed the proposed model with better quality data, in addition to the appropriate tuning of hyperparameters and selection of features.ResultsThe models’ performances with each algorithm are extensively evaluated with several positive and negative performance metrics along with runtime. Gradient boosting is found to have the overall best performance with 98.80% accuracy and 98.50% precision, recall and F1-score for each.ConclusionsThe proposed model with gradient boosting bettered in most metrics compared with several recent similar works, suggesting its efficacy in predicting liver disease. It can be further applied to predict other diseases with the commonality of predicate indicators.

The role of membrane filtration in wastewater treatment

AbstractWater is vital for the sustainable growth of human communities. Due to the greatest population and significant economic development, demands on water and purification have significantly risen. Classical wastewater treatment procedures have made some progress over the years in purifying effluents for disposal, but they fall lacking in the way they deeply control emerging harmful substances. The potential to reuse treated wastewater for home, agricultural, and industrial needs depends on breakthroughs in purifying technologies. One of the most recent breakthroughs to be implemented effectively in reducing contaminants to desirable levels is membrane technology. Nanofiltration, microfiltration, reverse osmosis, ultrafiltration, and membrane bioreactors are examples of widely utilized membrane processes depending on features such as size or charge. Over traditional treatment, membrane technology offers various benefits. This review article discusses a number of membrane‐related topics, including membrane categorizing, application in various fields, benefits, and drawbacks. Furthermore, offers an outlook of the filtering membranes that will be used in the future. Smart membranes have gained interest because of their tunable permeability and selectivity characteristics. Technological advancements in the media that are utilized in activated growth membrane bioreactors. Modified interfacial polymerization approaches have been investigated in addition to adsorptive ultrafiltration mixed matrix membranes to enhance the performance of the polyamide thin‐film composite and thin‐film nanocomposite membranes.

Modulating Ionic Linkages in the Heterocyclic Sulfated Polysaccharide Carrageenan for Enhanced Selectivity Against Amelanotic Melanoma Cells

AbstractThe present research is the use of Heterocyclic sulfated polysaccharides, exemplified by carrageenan, with the high selective anti‐cancer against melanoma cancer. This study focuses on optimizing the ionic linkage of carrageenan to enhance its selectivity and activity against skin cancer cells with the help of cell line assays and in‐silico studies. The cytotoxicity and selectivity of the optimized derivatives were assessed in‐vitro against skin cancer cells and normal skin cells. The barium‐linked carrageenan demonstrated significantly increased selectivity against skin cancer cells with a selectivity index of 45, however, the reference drug was found with a selectivity index of 23. The selectivity of the compound 3 (Ba ion linked) was analysed through the ion dissociation energy and found that the more energy is require to dissoxiate in the ion, which signifiacnlty absorbs inside the cell because of non‐ionic form. The selectivity sequence of K+<Ca2+<Ba2+ as a targeted anti‐cancer agent. The present research unfold the importance of ion linkage of chromophore, to get the more active and selective compounds, which will be a path to get the ion activity relationship of various drugs without interfering the chromophore. The findings suggest a promising avenue for the development of targeted therapies with the change of ions, emphasizing the potential of heterocyclic sulfated polysaccharides in the field of cancer treatment with enhanced tuning of selectivity. The further scope is the gel formation of the Ba2+ carrageenan which will be the first API (Active Pharmaceuitical Ingredients) based formulation for skin cancer.

AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.

Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1, EPHA2, CLSPN, OLFML3, and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ.

Asymmetric aggregation enables red-light CO2 reduction with tunable activity and selectivity by intermolecular electronic coupling

Photocatalytic CO2 reduction to value-added chemicals is a promising way to simultaneously mitigate environmental and energy issues. Integrating low-energy red light harvesting, fast charge separation, and robust CO2 activation in a photosystem is a crucial prerequisite for highly efficient CO2 photoreduction but remains a huge challenge. Herein, we constructed an aggregated [Ru(2, 2'-bipyridine)3]Cl2 (denoted as Ru(bpy)3Cl2) photosystem. Various characterizations combining molecular dynamic simulations and theoretical calculations confirm that the electronic coupling between adjacent Ru(bpy)3Cl2 molecules can induce orbital hybridization, thus broadening the light absorption edge to the red light region. Meanwhile, the asymmetric aggregation of Ru(bpy)3Cl2 enables exciton dissociation and charge transfer through a dipole polarization effect. Significantly, this dipole polarization can also upshift the d-orbital of Ni catalytic centers, greatly strengthening the activation of CO2 and lowering the formation energy barrier of COOH* intermediates. Consequently, the apparent quantum yield (AQY) of aggregates for CO2-to-CO reduction reaches 4.2% at 610 nm, significantly higher than the AQYs of other reported photosystems at wavelengths ≥ 600 nm. More importantly, the catalytic efficiency and selectivity of CO2 reduction can be rationally tuned by controlling the aggregated degrees of Ru(bpy)3Cl2. This work highlights the key role of Ru(bpy)3Cl2 aggregation in prompting red-light harvesting and CO2 activation, which may help boost CO2 photoreduction efficiency from a fresh perspective.

Early detection of chronic kidney disease using eurygasters optimization algorithm with ensemble deep learning approach

Chronic kidney disease (CKD) emerges as a global health problem with high morbidity and mortality rates, and it induces other diseases. Patients often fail to notice these diseases because there are no obvious symptoms during the earlier stage of CKD. Earlier diagnosis of CKD allows them to receive early medical intervention to improve the disease progression and outcome. Earlier stratification of CKD can affect the health care provided to patients through various options, such as kidney transportation, hemodialysis, or pharmacological care in milder cases. Recently, deep learning (DL) and machine learning (ML) approaches have gained significance in the domain of medical diagnoses due to their high prediction accuracy. DL and ML approaches can successfully aid physicians in achieving these goals due to their accurate and fast detection performance. The performance of the proposed technique mainly depends on selecting the suitable algorithms and appropriate features. Thus, the study introduces an Eurygasters Optimization Algorithm with an Ensemble DL CKD detection (EOAEDL-CKDD) method. The EOAEDL-CKDD method aims to detect and classify the presence of CKD using feature selection and hyperparameter tuning strategies. The EOAEDL-CKDD method applies a min-max scalar to convert the data into a uniform format to achieve this. The EOAEDL-CKDD technique exploits EOA to select features. This is followed by an ensemble of long short-term memory (LSTM), bidirectional gated recurrent unit (BiGRU), and bidirectional LSTM (BiLSTM) models that are used for the CKD detection process. Finally, a shuffled frog leap algorithm (SFLA) based hyperparameter selection process is carried out to choose the ensemble models' hyperparameter values optimally. The empirical assessment of the EOAEDL-CKDD method is tested on the benchmark CKD dataset. The experimental values highlighted that the EOAEDL-CKDD technique gains an optimal detection rate compared to existing models.

Reversibly Reactive Affinity Selection-Mass Spectrometry Enables Identification of Covalent Peptide Binders.

Covalent peptide binders have found applications as activity-based probes and as irreversible therapeutic inhibitors. Currently, there is no rapid, label-free, and tunable affinity selection platform to enrich covalent reactive peptide binders from synthetic libraries. We address this challenge by developing a reversibly reactive affinity selection platform termed ReAct-ASMS enabled by tandem high-resolution mass spectrometry (MS/MS) to identify covalent peptide binders to native protein targets. It uses mixed disulfide-containing peptides to build reversible peptide-protein conjugates that can enrich for covalent variants, which can be sequenced by MS/MS after reduction. Using this platform, we identified covalent peptide binders against two oncoproteins, human papillomavirus 16 early protein 6 (HPV16 E6) and peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 protein (Pin1). The resulting peptide binders efficiently and selectively cross-link Cys58 of E6 at 37 °C and Cys113 of Pin1 at room temperature, respectively. ReAct-ASMS enables the identification of highly selective covalent peptide binders for diverse molecular targets, introducing an applicable platform to assist preclinical therapeutic development pipelines.

Influence of Silsesquioxane-Containing Ultra-Thin Polymer Films on Metal Oxide Gas Sensor Performance for the Tunable Detection of Biomarkers

Certain biomarkers in exhaled breath are indicators of diseases in the human body. The non-invasive detection of such biomarkers in human breath increases the demand for simple and cost-effective gas sensors to replace state-of-the-art gas chromatography (GC) machines. The use of metal oxide (MOX) gas sensors based on thin-film structures solves the current limitations of breath detectors. However, the response at high humidity levels, i.e., in the case of exhaled human breath, significantly decreases the sensitivity of MOX sensors, making it difficult to detect small traces of biomarkers. We have introduced, in previous work, the concept of a hybrid gas sensor, in which thin-film-based MOX gas sensors are combined with an ultra-thin (20–30 nm) polymer top layer deposited by solvent-free initiated chemical vapor deposition (iCVD). The hydrophobic top layer enables sensor measurement in high-humidity conditions as well as the precise tuning of selectivity and sensitivity. In this paper, we present a way to increase the hydrogen (H2) sensitivity of hybrid sensors through chemical modification of the polymer top layer. A poly(1,3,5,7-tetramethyl-tetravinylcyclotetrasiloxane) (PV4D4) thin film, already applied in one of our previous studies, is transformed into a silsesquioxane-containing top layer by a simple heating step. The transformation results in a significant increase in the gas response for H2 ~709% at an operating temperature of 350 °C, which we investigate based on the underlying sensing mechanism. These results reveal new pathways in the biomedical application field for the analysis of exhaled breath, where H2 indicates gastrointestinal diseases.

A metagenome-derived artificial intelligence modeling framework advances the predictive diagnosis and interpretation of petroleum-polluted groundwater

Groundwater (GW) quality monitoring is vital for sustainable water resource management. The present study introduced a metagenome-derived machine learning (ML) model aimed at enhancing the predictive understanding and diagnostic interpretation of GW pollution associated with petroleum. In this framework, taxonomic and metabolic profiles derived from GW metagenomes were combined for use as the input dataset. By employing strategies that optimized data integration, model selection, and parameter tuning, we achieved a significant increase in diagnostic accuracy for petroleum-polluted GW. Explanatory artificial intelligence techniques identified petroleum degradation pathways and Rhodocyclaceae as strong predictors of a pollution diagnosis. Metagenomic analysis corroborated the presence of gene operons encoding aminobenzoate and xylene biodegradation within the de novo assembled genome of Rhodocyclaceae. Our genome-centric metagenomic analysis thus clarified the ecological interactions associated with microbiomes in breaking down petroleum contaminants, validating the ML-based diagnostic results. This metagenome-derived ML framework not only enhances the predictive diagnosis of petroleum pollution but also offers interpretable insights into the interaction between microbiomes and petroleum. The proposed ML framework demonstrates great promise for use as a science-based strategy for the on-site monitoring and remediation of GW pollution

BERT - Based Detection and Classification of Algorithmically Generated Domains in Malware Communication

Malware threats. Particularly ransomware, often employ Algorithmically Generated Domains (AGD) for communication with Command & Control (C&C) servers. This paper introduces a novel approach for AGD detection using the Bidirectional Encoder Representations from Transformer (BERT) model, eliminating the need for intricate feature selection or hyperparameter tuning. The proposed method effectively addresses the challenge posed by sophisticated domain generation techniques, including dictionary-based and random character approaches. Experimental results demonstrate the superior performance of the BERT model in both AGD detection and classification tasks, achieving a precision, recall, and accuracy score of 0.99. The approach proves effective against diverse domain generation algorithms, enhancing the current state-of-the-art methods for securing networks against evolving malware threats.

Electrodeposited Silver Dendrites on Laser‐Induced Graphene for Electrochemical Detection of Nitrate with Tunable Sensor Properties

AbstractLaser‐induced graphene (LIG) is a promising technology enabling cost‐effective, scalable, and high surface area 3D‐porous graphene electrodes for electrochemical applications. Nitrate in water bodies is a harmful contaminant to humans and the ecosystems. Its detection by electrochemical sensors is challenging due to the interference from nitrite. Herein, for the first time, a LIG‐based electrochemical sensor modified with electrodeposited silver dendrites (EdAg/LIG) without using surfactants is proposed for the detection of nitrate with tunable selectivity and sensitivity. The modified electrode surface is extensively characterized by spectroscopic and electrochemical methods and the underlying mechanism for the formation of dendrites is substantiated. The developed EdAg dendrites/LIG electrode shows excellent sensing properties for the detection of nitrate at pH 2. The interference with nitrite in acidic media is eliminated by implementing a novel strategy to shift the working pH of the electrode to 7. The achieved sensor properties at both pH values surpass other LIG‐based sensors with limit of detection of 0.46 at pH 2 and 5.53 µm at pH 7. The developed sensor also shows good recovery characteristics in mineral, tap, and groundwater across a wide range of concentrations and also demonstrates good stability under temperature fluctuations and deformations.

Predicting Graduation Outcomes: Decision Tree Model Enhanced with Genetic Algorithm

This research aims to improve the accuracy of predicting student permit results in the digital era by utilizing machine learning techniques. The main focus is the use of a Decision Tree (DT) model optimized with a Genetic Algorithm (GA) to overcome the limitations of accuracy and testing of conventional methods. This research began with collecting student academic data, followed by preprocessing to eliminate incompleteness and organize the data format. The DT model is then built and optimized with GA, which is inspired by biological evolutionary processes to improve feature selection and parameter tuning. The results show a significant increase in prediction accuracy, from 86.19% to 87.68%, and an increase in the Area Under Curve (AUC) value from 0.755% to 0.788%. This research not only proves the effectiveness of GA integration in improving DT models, but also paves the way for the application of evolutionary techniques in educational data analysis and other fields. The main contributions of this research include the development of more accurate prediction models and practical applications in educational contexts, with the hope of assisting educational institutions in making more informed decisions for their students.

Blue electroluminescence from homoleptic Ir(III) carbene phosphors and peripheral decorated multiple resonance boron emitters

Blue phosphors are indispensable in constructing organic light-emitting diodes (OLEDs). Herein, we reported two Ir(III) complexes, namely f-CN1 and f-CN2, with asymmetrically arranged benzo[d]imidazolylidenes, to which the peri-cyano substituent has concurrently induced both the selective aryl cyclometalation and emission tuning in giving one single blue Ir(III) phosphor. With the aforementioned ‘one stone for two birds’ tactics, these carbene complexes exhibited photoluminescent peak (λmax) at ∼472 nm and excellent quantum yields (83–100 %) in doped thin films. Consequently, the fabricated OLED devices delivered maximum external quantum efficiencies (EQEmax) of 23.6 % and 20.2 %, respectively. Furthermore, hyper-OLED devices with f-CN1 and multi-resonant TADF terminal emitter m-DiNBO displayed an EQEmax of 30.8 % with CIEx,y coordinates of (0.13, 0.10), together with impressive EQE of 26.9 % at practical brightness of 100 cd m−2.

Selective active resonance tuning for multi-mode nonlinear photonic cavities

Resonant enhancement of nonlinear photonic processes is critical for the scalability of applications such as long-distance entanglement generation. To implement nonlinear resonant enhancement, multiple resonator modes must be individually tuned onto a precise set of process wavelengths, which requires multiple linearly-independent tuning methods. Using coupled auxiliary resonators to indirectly tune modes in a multi-resonant nonlinear cavity is particularly attractive because it allows the extension of a single physical tuning mechanism, such as thermal tuning, to provide the required independent controls. Here we model and simulate the performance and tradeoffs of a coupled-resonator tuning scheme which uses auxiliary resonators to tune specific modes of a multi-resonant nonlinear process. Our analysis determines the tuning bandwidth for steady-state mode field intensity can significantly exceed the inter-cavity coupling rate g if the total quality factor of the auxiliary resonator is higher than the multi-mode main resonator. Consequently, over-coupling a nonlinear resonator mode to improve the maximum efficiency of a frequency conversion process will simultaneously expand the auxiliary resonator tuning bandwidth for that mode, indicating a natural compatibility with this tuning scheme. We apply the model to an existing small-diameter triply-resonant ring resonator design and find that a tuning bandwidth of 136 GHz ≈ 1.1 nm can be attained for a mode in the telecom band while limiting excess scattering losses to a quality factor of 106. Such range would span the distribution of inhomogeneously broadened quantum emitter ensembles as well as resonator fabrication variations, indicating the potential for the auxiliary resonators to enable not only low-loss telecom conversion but also the generation of indistinguishable photons in a quantum network.

U-trustworthy Models. Reliability, Competence, and Confidence in Decision-Making

With growing concerns regarding bias and discrimination in predictive models, the AI community has increasingly focused on assessing AI system trustworthiness. Conventionally, trustworthy AI literature relies on the probabilistic framework and calibration as prerequisites for trustworthiness. In this work, we depart from this viewpoint by proposing a novel trust framework inspired by the philosophy literature on trust. We present a precise mathematical definition of trustworthiness, termed U-trustworthiness, specifically tailored for a subset of tasks aimed at maximizing a utility function. We argue that a model’s U-trustworthiness is contingent upon its ability to maximize Bayes utility within this task subset. Our first set of results challenges the probabilistic framework by demonstrating its potential to favor less trustworthy models and introduce the risk of misleading trustworthiness assessments. Within the context of U-trustworthiness, we prove that properly-ranked models are inherently U-trustworthy. Furthermore, we advocate for the adoption of the AUC metric as the preferred measure of trustworthiness. By offering both theoretical guarantees and experimental validation, AUC enables robust evaluation of trustworthiness, thereby enhancing model selection and hyperparameter tuning to yield more trustworthy outcomes.

Heart disease prediction utilizing machine learning techniques

Heart disease is an international public health issue and a significant health risk for many people. The World Health Organization (WHO) reported that heart disease has been identified as one of the primary causes of death. Owing to its rapid development and application in many areas, Machine Learning has become an effective technique for predicting heart disease. Machine learning, along with large-scale medical data and advanced algorithms, can assist healthcare professionals in accurately predicting the risk of heart disease, thus providing early intervention and treatment for patients. This research paper uses the “heart_2020_cleaned.csv” dataset, containing 319795 instances and 18 attributes, of which 70% of instances were randomly selected for the training set and 30% for testing. Applying machine learning algorithms in data mining such as Decision Tree (DT), LightGBM, Random Forest (RF) and Logistic Regression (LR) to forecast heart disease. Before constructing models, data cleaning, feature selection and hyperparameter tuning processes were done, aiming to explore the potential patterns among data. Comparative Analysis was conducted on the external test set to compare the prediction performance of different models at the same level. The result reported that the highest accuracy achieved with LightGBM was 76.9%, followed by Logistic Regression and Random Forest, with Decision Tree being the worst.

Faradaic and Non-Faradaic Self-Discharge Mechanisms in Carbon-Based Electrochemical Capacitors.

Electrochemical capacitors (ECs) play a crucial role in electrical energy storage, offering great potential for efficient energy storage and power management. However, they face challenges such as moderate energy densities and rapid self-discharge. Addressing self-discharge necessitates a fundamental understanding of the underlying processes. This review sets itself apart from other reviews by focusing on the basic principles of self-discharge processes in carbon-based ECs, particularly examining the nature of the process and the involvement of redox reactions. This study delineates the potential conditions for various self-discharge processes and proposes plausible criteria for differentiation, complemented by mathematical modeling. Additionally, the model selection, curve fitting, and effective tuning methods are explored to control self-discharge processes.