Power Analysis Research Articles

Techno-economic of Residential Application for Grid-Connected Photovoltaic-Battery under Net Energy Metering

Nowadays, the demand for electrical energy is increasing due to the application of electrical equipment such as air conditioning, refrigerators, television, computers etc. Grid-connected photovoltaic is a network that allows for significant penetration of electricity by PV generation into the national utility grid under Net Energy Metering (NEM) incentive. Under NEM incentive, the owner can offset the energy for 10 years to reduce the electricity billing. Unfortunately, after that, the self-consumption (SELCO) program is implemented. Under SELCO program, the energy generated by PV will be consumed by the owner and there is no rebate for excess energy to the grid. This paper represents the techno-economic analysis of different rated power of PV design installed for residential applications. The 1.04kWp, 1.56kWp and 2.08kWp PV are designed to charge 5.544kWh battery and simulated by using PVsyst software. The payback period is calculated under Net Energy Metering (NEM) 3.0 incentive. As a result, 1.56kWp is the optimize sizing for grid-connected PV-battery with electricity billing saving at RM730.20 per year under one-on-one program and RM660.61 under SELCO program. Thus, return on investment (ROI) of this system design is 16.5 years.

Efficacy of facilitated tucking position and Reiki given to preterm infants during orogastric tube insertion: A randomised controlled trial.

This research was conducted to evaluate the effects of Reiki and facilitated tucking position on pain, stress and physiologic parameters in preterm infants during orogastric tube (OGT) insertion. The study used a single-blind, parallel-group randomised controlled experimental design. It was carried out in the neonatal intensive care unit of a hospital in Niğde/Turkey between February 2022 and January 2023. A total of 45 preterm infants, who met the study criteria and whose sample size was determined according to power analysis, were randomly divided into three groups. As an intervention, the facilitated tucking position was applied to the first group, and Reiki was applied to the second group during OGT insertion. Routine application continued in the control group. The Infant Introductory Information Form, Physiological Parameter Follow-up Chart, Infant Stress Scale (ISS) and Premature Infant Pain Profile Scale-Revised (PIPP-R) were used for data collection. Significance was accepted as P < 0.05 in the statistical analysis. It was determined that the infants were homogeneously distributed between the experimental and control groups (P > 0.05). Infants who received the facilitated tucking position and Reiki intervention had better physiological stability compared to the control group (P < 0.05). The group with the lowest average stress (1.53 ± 0.99) and pain scores (4.06 ± 1.22) during OGT insertion was the facilitated tucking group (P < 0.001). After the procedure, stress and pain scores in both the facilitated tucking group and the Reiki group were found to be significantly lower than those in the control group (P < 0.001). The results of the study showed that the facilitated tucking position during OGT insertion was especially effective in reducing the pain and stress of infants. Both the facilitated tucking position and Reiki were determined to be effective interventions in reducing the pain and stress of infants more quickly after the procedure. The results of study contribute to the recommendation that NICU nurses should include non-pharmacological methods to decrease the pain of preterm infants during procedural pain.

Modulation of Brain Activities in Healthy Individuals by Acupuncture at Quchi (LI11).

This research investigated the modulation of acupuncture at Quchi (LI11) on the brain activities in healthy individuals. Sub-bands power and EEG microstate analysis were carried out at pre-acupuncture, acupuncture, needle retaining and post-acupuncture periods in both the acupuncture group (n = 16) and control group (n = 18). Four microstate classes (A-D) were derived from the clustering procedure. Regression analysis was conducted, together with a two-way repeated measures ANOVA, which was then followed by Bonferroni correction. In the acupuncture group, we found the beta power during the acupuncture periods was significantly reduced. The channel-by-channel analysis revealed that acupuncture at LI11 mainly altered the power of delta, theta, and alpha waves in specific brain regions. The delta power increased predominantly in parietal, occipital, and central lobes, while theta and alpha power decreased predominantly in temporal, frontal, parietal, and occipital lobes. During the acupuncture period, participants in the acupuncture group showed a significant increase in both duration and contribution of microstate A, as well as the bidirectional transition probabilities A and B/D. Microstate analysis showed that acupuncture at LI11 significantly enhances the activity of microstate A and potentially strengthens the functional connectivity between the auditory network and either the visual network or the dorsal attention network. These correlational results indicate that acupuncture at LI11 mainly affects activities of the frontal, temporal, parietal, and occipital lobes. These findings highlight the potential of microstate as neuroimaging evidence and a specific index for elucidating the neuromodulatory effects of acupuncture at LI11.

On the limits of economic activity: bridging degrowth and modern monetary theory for socio-ecological sustainability and justice

This paper advances recent work exploring synergies and tensions between two schools of political-economic thought—degrowth and modern monetary theory (MMT)—and posits that this synergy warrants closer attention from International Political Economy and Environment (IPEE) scholars. Thus far, degrowth policies have largely been proposed upon the assumption that governments are limited in budgetary terms. MMT theorists have offered an alternative perspective on fiscal policy space but generally provide insufficient ecological considerations. This paper asks how each school’s underlying assumptions about the limits of economic activity might inform the other’s framework of action. We find that while there are tensions between the two schools’ economic philosophies, there are possibilities for cross-fertilization, which ought to interest IPEE scholars. MMT could engage degrowth’s perspective of ecological limits and global socio-ecological justice in its discussions about what economies can ‘afford’, whereas degrowth could use MMT insights to shift from a ‘pay for’ to a ‘resource’ frame, while centering abundance before limits. Meanwhile, IPEE can provide insights to an MMT-informed degrowth transition by grounding this debate in material analyses of class and power. Our findings suggest that degrowth and MMT could provide a normative direction for IPEE research centered around meeting material needs within planetary boundaries.

Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction

The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to Transformer. By correcting the prediction errors of SWAT using Transformer, the two models are effectively coupled. Monthly runoff data at Yan’an and Ganguyi stations on Yan River, a first-order tributary of the Yellow River Basin, were used to evaluate the proposed model’s performance. The results indicated that SWAT performed well in predicting high flows but poorly in low flows. In contrast, Transformer was able to capture low-flow period information more accurately and outperformed SWAT overall. SWAT-Transformer could correct the errors of SWAT predictions and overcome the limitations of a single model. By integrating SWAT’s detailed physical process portrayal with Transformer’s powerful time-series analysis, the coupled model significantly improved streamflow prediction accuracy. The proposed models offer more accurate and reliable predictions for optimal water resource management, which is crucial for sustainable economic and societal development.

ZeroD-fender: A Resource-aware IoT Malware Detection Engine via Fine-grained Side-channel Analysis

In early 2023, cyberattacks experienced a significant rise due to unknown (zero-day) malware targeting Internet of Things (IoT) devices. To tackle the challenge of zero-day detection within a highly resource-constrained IoT environment, we propose a novel design that utilizes fine-grained power side-channel analysis with deep learning techniques. Our approach introduces an innovative concept called multiscale feature extraction to identify the most representative malware features across diverse architectures, thereby enhancing deep learning based detection performance against zero-day malware. Specifically, we employ a fine-grained power side-channel analysis of more than 120,000 honeypot-collected malware files across a hierarchy of commands , functions , and modules to identify the unique zero-day malware behaviors. With these identified features to train our model, ZeroD-fender’s performance in detecting zero-day malware has significantly improved. In pursuit of on-device detection, we present a resource-aware online inference customization framework. This framework features our lightweight network, ThingNetV2, which uses specialized 1-D depthwise separable convolution paired with h-swish activation, leading to significant resource savings. By applying the fine-grained power analysis, ZeroD-fender demonstrates a detection rate of 95.88% across various architecture zero-day malware, achieving detection speeds ranging between 16.083 ms and 23.961 ms , depending on the specific scenario.

Area-Time-Efficient Secure Comb Scalar Multiplication Architecture Based on Recoding

With the development of mobile communication, digital signatures with low latency, low area, and high security are in increasing demand. Elliptic curve cryptography (ECC) is widely used because of its security and lightweight. Elliptic curve scalar multiplication (ECSM) is the basic arithmetic in ECC. Based on this background information, we propose our own research objectives. In this paper, a low-latency and low-area ECSM architecture based on the comb algorithm is proposed. The detailed methodology is as follows. The recoding-k algorithm and randomization-Z algorithm are used to improve security, which can resist sample power analysis (SPA) and differential power analysis (DPA). A low-area multi-functional architecture for comb is proposed, which takes into account different stages of the comb algorithm. Based on this, the data dependency is considered and the comb architecture is optimized to achieve a uniform and efficient execution pattern. The interleaved modular multiplication algorithm and modified binary inverse algorithm are used to achieve short clock cycle delay and high frequency while taking into account the need for a low area. The proposed architecture has been implemented on Xilinx Virtex-7 series FPGA to perform ECSM on 256-bits prime field GF(p). In the hardware architecture with only 7351 slices of resource usage, a single ECSM only takes 0.74 ms, resulting in an area-time product (ATP) of 5.41. The implementation results show that our design can compete with the existing state-of-the-art engineering in terms of performance and has higher security. Our design is suitable for computing scenarios where security and computing speed are required. The implementation of the overall architecture is of great significance and inspiration to the research community.

Unpacking Needs Protection

Most of the previous attacks on Dilithium exploit side-channel information which is leaked during the computation of the polynomial multiplication cs1, where s1 is a small-norm secret and c is a verifier's challenge. In this paper, we present a new attack utilizing leakage during secret key unpacking in the signing algorithm. The unpacking is also used in other post-quantum cryptographic algorithms, including Kyber, because inputs and outputs of their API functions are byte arrays. Exploiting leakage during unpacking is more challenging than exploiting leakage during the computation of cs1 since c varies for each signing, while the unpacked secret key remains constant. Therefore, post-processing is required in the latter case to recover a full secret key. We present two variants of post-processing. In the first one, a half of the coefficients of the secret s1 and the error s2 is recovered by profiled deep learning-assisted power analysis and the rest is derived by solving linear equations based on t = As1 + s2, where A and t are parts of the public key. This case assumes knowledge of the least significant bits of t, t0. The second variant uses lattice reduction to derive s1 without the knowledge of t0. However, it needs a larger portion of s1 to be recovered by power analysis. We evaluate both variants on an ARM Cortex-M4 implementation of Dilithium-2. The experiments show that the attack assuming the knowledge of t0 can recover s1 from a single trace captured from a different from profiling device with a non-negligible probability.

Data-model driven probability model of branch power flow for distribution networks and its application to analysis of overload and line loss

The fluctuation of highly penetrated distributed generations (DGs) in distribution networks (DNs) increases branch overload risks, which makes the analysis uncertainty of branch power flow more complex. The probability analysis of branch power can grasp the power flow operation characteristics under uncertain conditions, which supports the power flow optimization management and ensures the safe and economic operation of DNs. Therefore, this paper proposes a data-model driven probability analysis method for branch power flow in DNs. An approximate branch power model is first derived to reveal the analytical relationship between branch power and node power. Then, based on the branch power approximation model and the forecasting error, the datasets of branch apparent power are constructed to capture the complex nonlinear characteristics of the power flow. The non-Gaussian distribution characteristics of branch apparent power and line loss are described by the optimal probability fitting method. Finally, the probability level of branch overload is evaluated and the probability distribution of line loss is analyzed. The case studies demonstrate that the branch power approximation model is highly accurate, and the probability distribution of the branch apparent power presents different characteristics. The proposed method can quickly and accurately calculate the branch overload probability level.

Side-Channel Linearization Attack on Unrolled Trivium Hardware

This paper presents a new side-channel attack (SCA) on unrolled implementations of stream ciphers, with a particular focus on Trivium. Most conventional SCAs predominantly concentrate on leakage of some first rounds prior to the sufficient diffusion of the secret key and initial vector (IV). However, recently, unrolled hardware implementation has become common and practical, which achieves higher throughput and energy efficiency compared to a round-based hardware. The applicability of conventional SCAs to such unrolled hardware is unclear because the leakage of the first rounds from unrolled hardware is hardly observed. In this paper, focusing on Trivium, we propose a novel SCA on unrolled stream cipher hardware, which can exploit leakage of rounds latter than 80, while existing SCAs exploited intermediate values earlier than 80 rounds. We first analyze the algebraic equations representing the intermediate values of these rounds and present the recursive restricted linear decomposition (RRLD) strategy. This approach uses correlation power analysis (CPA) to estimate the intermediate values of latter rounds. Furthermore, we present a chosen-IV strategy for a successful key recovery through linearization. We experimentally demonstrate that the proposed SCA achieves the key recovery of a 288-round unrolled Trivium hardware implementation using 360,000 traces. Finally, we evaluate the performance of unrolled Trivium hardware implementations to clarify the trade-off between performance and SCA (in)security. The proposed SCA requires 34.5 M traces for a key recovery of 384-round unrolled Trivium implementation and is not applicable to 576-round unrolled hardware.

Nucleated red blood cell distribution in critically ill patients with acute pancreatitis: a retrospective cohort study

ObjectivesThis study examined the potential association between nucleated red blood cell (NRBC) levels and mortality in critically ill patients with acute pancreatitis (AP) in the intensive care unit, due to limited existing research on this correlation.MethodsThis retrospective cohort study utilized data from the MIMIC-IV v2.0 and MIMIC-III v1.4 databases to investigate the potential relationship between NRBC levels and patient outcomes. The study employed restricted cubic splines (RCS) regression analysis to explore non-linear associations. The impact of NRBC on prognosis was assessed using a generalized linear model (GLM) with a logit link, adjusted for potential confounders. Furthermore, four machine learning models, including Gradient Boosting Classifier (GBC), Random Forest, Gaussian Naive Bayes, and Decision Tree Classifier model, were constructed using NRBC data to generate risk scores and evaluate the potential of NRBC in predicting patient prognosis.ResultsA total of 354 patients were enrolled in the study, with 162 (45.8%) individuals aged 60 years or older and 204 (57.6%) males. RCS regression analysis demonstrated a non-linear relationship between NRBC levels and 90-day mortality. Receiver Operating Characteristic (ROC) analysis identified a 1.7% NRBC cutoff to distinguish survivor from non-survivor patients for 90-day mortality, yielding an Area Under the Curve (AUC) of 0.599, with a sensitivity of 0.475 and specificity of 0.711. Elevated NRBC levels were associated with increased risks of 90-day mortality in both unadjusted and adjusted models (all Odds Ratios > 1, P < 0.05). Assessment of various machine learning models with nine variables, including NRBC, Sex, Age, Simplified Acute Physiology Score II, Acute Physiology Score III, Congestive Heart Failure, Vasopressin, Norepinephrine, and Mean Arterial Pressure, indicated that the GBC model displayed the highest predictive accuracy for 90-day mortality, with an AUC of 0.982 (95% CI 0.970–0.994). Post hoc power analysis showed a statistical power of 0.880 in the study.ConclusionsElevated levels of NRBC are linked to an increased mortality risk in critically ill patients with AP, suggesting its potential for predicting mortality.

Capillary Blood Docosahexaenoic Acid Levels Predict Electrocardiographic Markers in a Sample Population of Premenopausal Women.

Introduction: The relationship between blood N-3 polyunsaturated fatty acid (PUFA) levels and cardiovascular health is known, but direct evidence that N-3 PUFA levels influence electrocardiographic (ECG) parameters is non-existent. In the study described herein, we investigated the relationship between anthropometric biomarkers and capillary blood PUFAs with ECG outputs in a sample population of healthy pre-menopausal women. Method: Twenty-three consenting females were recruited, with the study power analysis sufficiently demonstrated. Food intake, anthropometric and cardiovascular parameters were obtained. Capillary blood was collected for fatty acid chromatographic analysis. Results: Body mass index, haematocrit, heart rate (HR), mean arterial pressure (MAP) and ECG readings all fell within healthy ranges. Principal component analysis-mediated correlations were carried out controlling for combined Components 1 (age, body fat % and waist-to-hip ratio) and 2 (height, HR and MAP) as control variables. Docosahexaenoic acid (DHA) unequivocally decreased the QRS area under the curve (AUC-QRS) regardless of the impact of control variables, with each unit increase in DHA corresponding to a 2.3-unit decrease in AUC-QRS. Mediation analysis revealed a significant overall effect of DHA on AUC-QRS, with the impact of DHA on R wave amplitude accounting for 77% of the total observed effect. Discussion: Our new findings revealed an inverse relationship between AUC-QRS with capillary blood DHA, suggesting that the association between ventricular mass and its QRS depolarising voltage is mediated by DHA. Our findings bridge a knowledge gap on the relationship between ventricular mass and ventricular efficiency. Further research will confirm whether the relationship identified in our study also exists in diseased patients.

Quantification of core noise using coherent output method: experiment and result on turbofan demonstrator

New turbofan architectures have increasing bypass ratios which makes the core noise an more important source. An identification of its contribution in the total noise has been performed on a DGEN380 turbofan demonstrator using pressure measurement: The remote pressure probes with piezo-electric pressure sensors are installed at 14 locations in the nozzle. The far field noise was caught using 25 microphones array parallel to the engine axis. A coherent output power analysis based on multiple reference channels was applied to identify the active part of the combustion noise and in a reciprocal way the acoustic component in the duct propagated in the test room. The processing allows to calculate the far field contribution and directivity of the core noise. K-type thermocouples were implemented to quantify temperature fluctuations in the engine nozzle. The thermocouples were manufactured with two different wire diameters (from 25 to 80µm) which give different frequency responses. An in-situ compensation method was developed to correct the signal and increase the useful frequency range of the temperature measure. This allows to cover the whole core noise frequency range. This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No. 886554.

Bayesian inference for group-level cortical surface image-on-scalar regression with Gaussian process priors.

In regression-based analyses of group-level neuroimage data, researchers typically fit a series of marginal general linear models to image outcomes at each spatially referenced pixel. Spatial regularization of effects of interest is usually induced indirectly by applying spatial smoothing to the data during preprocessing. While this procedure often works well, the resulting inference can be poorly calibrated. Spatial modeling of effects of interest leads to more powerful analyses; however, the number of locations in a typical neuroimage can preclude standard computing methods in this setting. Here, we contribute a Bayesian spatial regression model for group-level neuroimaging analyses. We induce regularization of spatially varying regression coefficient functions through Gaussian process priors. When combined with a simple non-stationary model for the error process, our prior hierarchy can lead to more data-adaptive smoothing than standard methods. We achieve computational tractability through a Vecchia-type approximation of our prior that retains full spatial rank and can be constructed for a wide class of spatial correlation functions. We outline several ways to work with our model in practice and compare performance against standard vertex-wise analyses and several alternatives. Finally, we illustrate our methods in an analysis of cortical surface functional magnetic resonance imaging task contrast data from a large cohort of children enrolled in the adolescent brain cognitive development study.

State‐of‐the‐Art 3DES Pipelined Cryptographic Engine Design Against Side‐Channel Attacks

ABSTRACTIn this paper, a high‐speed, low‐latency, and high‐security hardware‐implemented triple data encryption standard (3DES) cryptographic algorithm is proposed for securing data privacy. In order to achieve a high throughput for the 3DES architecture, the whole circuit is optimized with a 24‐stage pipelined design at first. For breaking the correlation between the processed data and power dissipation of the 3DES circuit against differential power analysis (DPA) attacks, two pseudo‐random number generators (PRNGs) are embedded to randomly alter the number of pipelined stages and data substitution locations within the 3DES circuit, respectively. Under such a circumstance, the proposed 3DES circuit is able to achieve high performance and strong robustness against DPA attacks without causing much overhead. As shown in the results, under the synthesis of SMIC 14‐nm process design kits (PDK), the clock frequency, area, power dissipation, and throughput of the proposed 3DES circuit, respectively, are achieved as 1.2 GHz, 26,435 m2, 21.05 mW, and 64 Gbps. Furthermore, when DPA attacks are executed on the proposed 3DES circuit, the corresponding secret key cannot be revealed even if 2 million plaintexts are enabled. In contrast, only 40,000 plaintexts are adequate to disclose the secret key of a regular pipelined 3DES circuit.

Brave Global Spaces: Researching Digital Health and Human Rights through Transnational Participatory Action Research

In this paper we reflect on our experience with applying Transnational Participatory Action Research (TPAR) to a multi-country study of digital health and human rights of young adults living with and affected by HIV in five low- and middle-income countries (LMICs), and identify some lessons learned for future projects. First, we propose a definition of TPAR based on our experience and our analysis of power in the project. We present an overview of the research design and implementation, which melded diverse working cultures and research methods. Next, we describe how we adapted outputs, working methods and terminology to meet the diverse and specific needs of civil society organizations, community-led networks and academics working in diverse national and transnational spaces. This required us to understand and adapt to the different temporalities at play. The creation of brave spaces and the development of an intersectional lens were key to addressing tensions that naturally emerged in our collaboration. Finally, we summarize lessons learns and challenges for the next stage of the project.

Oral Yeasts Carriage in Prediabetic Smokers and Nonsmokers

Habitual smoking and prediabetes are independent risk factors for increased oral yeasts carriage (OYC); however, no studies have compared OYC amongst cigarette smokers and nonsmokers with and without prediabetes. The aim was to fill this research gap. Ninety-two participants were included and categorised into 4 groups: group 1, prediabetic (haemoglobin A1c [HbA1c] levels, 5.7%-6.4%) cigarette smokers; group 2, cigarette smokers without prediabetes; group 3, prediabetic nonsmokers; and group 4, nonsmokers without prediabetes. Patient demographics and HbA1c levels were recorded. Data on duration of smoking habit (pack-years) and family history of smoking were collected. Information on daily toothbrushing and flossing and most recent visit to a dentist/dental hygienist was gathered. Clinical and radiographic periodontal examination was performed and unstimulated whole salivary flow rate (UWSFR) was determined. OYC was assessed using the oral rinse sampling method. Power analysis was done, and group comparisons were performed. Logistic regression analysis was performed and P values <5% reflected statistical significance. Respectively, 23, 24, 22, and 23 individuals with comparable mean ages were included in groups 1, 2, 3, and 4. In groups 1 and 2, participants had a smoking history of (mean ± SD) 24.7 ± 3.2 and 10.6 ± 2.5 pack-years. Plaque index, clinical attachment loss, and probing depth were higher in groups 1 (P < .05), 2 (P < .05), and 3 (P < .05) than in group 4. Number of missing teeth was significantly higher in group 1 compared with groups 2 (P < .05), 3 (P < .05), and 4 (P < .05). There was no difference in UWSFR amongst the groups. OYC was greater in group 1 than in groups 2 (P < .05), 3 (P < .05), and 4 (P < .05). OYC was greater in groups 2 (P < .05) and 3 (P < .05) than in group 4. In prediabetic cigarette smokers, OYC appears to be influenced by hyperglycaemia, whilst in nondiabetic smokers, the severity of periodontal inflammation appears to be the determining factor in OYC.

The role of effect size and significance test in research design and analysis

Effect size is a straightforward method for measuring the difference between two groups and plays a critical role in research design and significance testing. This paper addresses the concept of effect size, explaining what it is, how it is calculated, and how it can be interpreted. It also explores related topics, such as statistical significance, meta-analysis, and power analysis. Effect sizes can be understood in the context of confidence intervals, which provide similar information to statistical significance tests but focus more on the magnitude of the effect rather than the size of the sample.

Ordinal regression models made easy: A tutorial on parameter interpretation, data simulation and power analysis.

Ordinal data such as Likert items, ratings or generic ordered variables are widespread in psychology. These variables are usually analysed using metric models (e.g., standard linear regression) with important drawbacks in terms of statistical inference (reduced power and increased type-1 error) and prediction. One possible reason for not using ordinal regression models could be difficulty in understanding parameters or conducting a power analysis. The tutorial aims to present ordinal regression models using a simulation-based approach. Firstly, we introduced the general model highlighting crucial components and assumptions. Then, we explained how to interpret parameters for a logit and probit model. Then we proposed two ways for simulating data as a function of predictors showing a 2 × 2 interaction with categorical predictors and the interaction between a numeric and categorical predictor. Finally, we showed an example of power analysis using simulations that can be easily extended to complex models with multiple predictors. The tutorial is supported by a collection of custom R functions developed to simulate and understand ordinal regression models. The code to reproduce the proposed simulation, the custom R functions and additional examples of ordinal regression models can be found on the online Open Science Framework repository ( https://osf.io/93h5j).

Redefining the Symphony of Light Aromatic Synthesis Beyond Fossil Fuels: A Journey Navigating through a Fe-Based/HZSM-5 Tandem Route for Syngas Conversion.

The escalating concerns about traditional reliance on fossil fuels and environmental issues associated with their exploitation have spurred efforts to explore eco-friendly alternative processes. Since then, in an era where the imperative for renewable practices is paramount, the aromatic synthesis industry has embarked on a journey to diversify its feedstock portfolio, offering a transformative pathway toward carbon neutrality stewardship. This Review delves into the dynamic landscape of aromatic synthesis, elucidating the pivotal role of renewable resources through syngas/CO2 utilization in reshaping the industry's net-zero carbon narrative. Through a meticulous examination of recent advancements, the current Review navigates the trajectory toward admissible aromatics production, highlighting the emergence of Fischer-Tropsch tandem catalysis as a game-changing approach. Scrutinizing the meliorated interplay of Fe-based catalysts and HZSM-5 molecular sieves would uncover the revolutionary potential of rationale design and optimization of integrated catalytic systems in driving the conversion of syngas/CO2 into aromatic hydrocarbons (especially BTX). In essence, the current Review would illuminate the path toward cutting-edge research through in-depth analysis of the transformative power of tandem catalysis and its capacity to propel carbon neutrality goals by unraveling the complexities of renewable aromatic synthesis and paving the way for a carbon-neutral and resilient tomorrow.