Low Cost Research Articles

An environmentally friendly and scalable manufacturing route to the high performance composite polymer electrolytes for lithium-metal batteries

Environmentally friendly manufacturing of flexible all-solid-state electrolytes in large-scale and low cost is important for market entering of lithium metal batteries. Herein, a simple and practical solvent-free route to the high performance composite polymer electrolyte is proposed by infiltrating the hot-molten polyether polymer (F127)/Li-salt (LiTFSI) slurry into a thin cellulose fibrous membrane. The good compatibility between the slurry and cellulose membrane allows the formation of a highly dense F127/LiTFSI/cellulose composite electrolyte with a thin thickness of 28 μm. The solvent-free feature without side-reaction as well as the good mechanical stability of such electrolyte makes great significance to ensure the safety and stability of the batteries. Therefore, its Li symmetric cell can stably operate with an ultralong lifespan of more than 3600 h without dendritic growth of metal lithium, and the LiFePO4//Li cell can also deliver an excellent cycling stability with an initial capacity of 130.1 mAh g−1 and a capacity retention of 84.11 % after more than 260 cycles at 0.5C and 60 °C. Additionally, the performance of high-loading cathode full cell and the flexible pouch cell further confirm its excellent stability and safety for practical use.

Re-constructing the River Bed Using the Streamline-Generation Method

River bed reconstruction plays an essential part in supporting the hydrodynamic simulation and understanding the morphological processes of a river. The streamlines can be solved using Laplace equations. The equation is first numerically solved in a computational environment and then adapted to the whole considered physical field to solve the resulting streamlines in a physical domain. One of the goals of this research is to determine the bottom line of the riverbed, and doing this through a field survey is very expensive, the methods presented in this research help a lot in reducing costs. By determining the concave line, the path of the flood in the river bed is determined, so one of the practical achievements and efficiencies of this research is flood trending at a low cost. In the present study, the bottom line is determined for the meandering Qinhe River, a distributary of the Yellow River, China, and Gaz River, located in Khuzestan Province, Iran. The method is based on the following steps:• Reconstruction of 2D river based on the Laplace equation.• Use the Finite Element Method to solve streamlines in the physical domain.• Use the Finite Difference Method to solve streamlines in the physical domain.

Review on Diabetes Mellitus: An Insight into the Current Scenarios, the Challenges of Therapy, and Application of Traditional Drugs

Abstract: Diabetes mellitus, an epidemic of the 21st century, is listed as one of the top ten causes of death around the world by the World Health Organization and is a complicated disease to deal with. Medically, a metabolic disorder characterized by elevated blood glucose levels and altered metabolism of biological macromolecules, mainly carbohydrates. Diabetes mellitus has a worldwide prevalence, and populations of all high, middle, as well as low-income countries, are affected by it. More than 400 million persons are currently living with diabetes. Many risk factors, such as lack of exercise, irregular diet, drinking, and smoking habits, as well as genetic factors, also contribute to the increased incidence of diabetes mellitus in the modern era. The conventional therapies for diabetes are based on insulin, insulin analogues, peptides, and oral hypoglycemics. The primary objective is to lower the elevated blood glucose levels and manage the distress to enhance the quality of life of the patient. The management of diabetes is very crucial to avoid complications having serious consequences such as the extra economic burden due to these complications. Conventional drug therapy has its own adverse effects, and its timely availability and cost are still an issue in many low and middle-income countries. Diabetes mellitus has been reported in various ancient kinds of literature along with various natural remedies (plants/herbals) useful in it. These plant-based remedies offer the benefits of lesser or no adverse effects, easy availability due to natural abundance, and low cost. Therefore, extensive research work is carried out in the past few decades to identify and understand the underlying principles of plant-based/herbal medicines to develop new potential lead molecules and a relatively safer, easily available and more economical alternative treatment therapy for diabetes mellitus.

Investigating the Accuracy of Boat Propeller Blade Components with Reverse Engineering Approach using Photogrammetry Method

This research aims to investigate the accuracy of 3D scanner by conducting a comparative analysis of AI-based photogrammetry method and Agisoft Metashape software, a more affordable and accessible approach compared to expensive commercial solutions. The proposed method uses photogrammetry and CAD (Computer-Aided Design) to measure propeller blade geometry through a three-stage process. In the first stage, the images are taken with a digital camera with calibrated and encoded targets. Additionally, reconstruct the propeller blades in Geomagic Studio. Taking photos of objects only takes a short time and is professional. Subsequently, in the second stage, the 2D images are processed using Agisoft Metashape software and Artificial Intelligence (AI) algorithms to generate an accurate 3-dimensional (3D) model. The third and final stage employs CAD programs to measure various radii of each blade in the 3D virtual model. To validate the accuracy of the measurements, the results are compared with those obtained through machine CMM measurements of each area on the propeller blade. The results show that this method successfully automates the entire reverse engineering process at low cost, lower shooting time, and producing 3D models with high accuracy. The validation shows that the 3D results using Agisoft Metashape have a smaller error (%) value compared to the 3D results using AI. Ultimately, Agisoft Metashape yields higher accuracy results compared to AI, but the results suggest that AI can serve as an alternative for the reverse engineering process in this modern era.

Description and analysis of Sigfox received signal strength indicator dataset by using statistical techniques

Low power wide area network (LPWAN) technology has expanded and is essential in the development of applications for the internet of things (IoT). The Sigfox LPWAN network is characterized by its long-range coverage, low cost and power consumption. In this article, a set of 5174 values is analyzed, containing 1606 null RSSI data, obtained with the Sipy module and MicroPython, which provide a coverage map of several points with a resolution of 200 meters deployed in Quito–Ecuador. It is evaluated the type of distribution to which the set of network measurements is adjusted and an optimal 900 MHz propagation model in suburban environments is determined from the measurements obtained from the known base station. As a result, the lost values of RSSI were predicted using the inverse normal distribution method in the original values, observing that they conform to a logistic distribution. The data from the base station were subjected to a data augmentation algorithm designed in MATLAB, determining that the stanford university interim (SUI) model reduces the precision error in the trend of the curve by not presenting changes greater than 5 dB, achieving a precision of 97% with respect to the fit of the curve of the data.

Tuning the coordination environment of sulfur on carbon surface via doping with iron for high-capacity gaseous elemental mercury capture

Enhancement of the formation of unsaturated short-chain sulfur represents the key point in enhancing the gaseous elemental mercury (Hg0) capture by sulfur-based sorbents, and yet the sulfur often agglomerates into long-chain species. Herein, by doping with iron cation as the regulator of the coordination environment of sulfur, the long-chain sulfur was successfully converted into short-chain sulfur on carbon. The iron-sulfur loaded carbon shows a high Hg0 adsorption capacity of 72.8 mg·g−1 (80 % breakthrough) and a low cost of $42.09/kg Hg, which are superior to various sulfur modified carbons and metal sulfides. The iron can not only immobilize more amount of sulfur on carbon surface, but also break the S-S bonds. The double interaction of iron and carbon with sulfur facilitates the formation of short-chain sulfur species for Hg0 adsorption. Density functional calculations suggest that S, Fe and C sites on surface are active centers for Hg adsorption. The Hg can tri-coordinate with S, Fe and C sites, which gives a high adsorption energy of − 219.3 kJ‧mol−1, confirming the strong reaction. This work provides a new in insights into the regulation of the coordination environment of sulfur, which allows the designing of high-capacity and low-cost sorbents for gaseous Hg0 removal.

Nanocrystal Materials for Resistive Memory and Artificial Synapses: Progress and Prospects.

Resistive random-access memory (RRAM) is considered to be the most promising next-generation non-volatile memory because of its low cost, low energy consumption, and excellent data storage characteristics. However, the on/off (SET/RESET) voltages of RRAM are too random to replace the traditional memory. Nanocrystals (NCs) offer an appealing option for these applications since they combine excellent electronic/optical properties and structural stability and can address the requirements of low-cost, large-area, and solution-processed technologies. Therefore, the doping NCs in the function layer of RRAM are proposed to localize the electric field and guide conductance filaments (CFs) growth. The purpose of this article is to focus on a comprehensive and systematical survey of the NC materials, which are used to improve the performance of resistive memory (RM) and optoelectronic synaptic devices and review recent experimental advances in NC-based neuromorphic devices from artificial synapses to light-sensory synaptic platforms. Extensive information related to NCs for RRAM and artificial synapses and their associated patents were collected. This review aimed to highlight the unique electrical and optical features of metal and semiconductor NCs for designing future RRAM and artificial synapses. It was demonstrated that doping NCs in the function layer of RRAM could not only improve the homogeneity of SET/RESET voltage but also reduce the threshold voltage. At the same time, it could still increase the retention time and provide the probability of mimicking the bio-synapse. NC doping can significantly enhance the overall performance of RM devices, but there are still many problems to be solved. This review highlights the relevance of NCs for RM and artificial synapses and also provides a perspective on the opportunities, challenges, and potential future directions.

Design of Interactive Online Teaching System for Big Data Computer Information System Based on Mobile Sensor Network

The combination of the Internet and computers has resulted in many new products, including smart homes. The emergence of smart homes can satisfy people's detection of the internal environment of the home, not only to ensure the continuity and safety of indoor monitoring, but also to provide convenience for people to manage their family life. This paper analyzes the current status of smart home research at home and abroad, and people's needs for homes, and builds and designs smart home systems based on Ethernet and ZigBee protocol wireless sensor networks connected to TCP/IP protocol. In this article, the ARM chip is used in the Android system to ensure that the system and functions are matched, and information is transmitted through the serial port of the development board. After receiving the corresponding sending signal, the wireless module will use ZigBee technology to send the corresponding signal to achieve the goal of controlling home appliances. We divides the control system into two parts, the Android Voice Control Plateform and the data collection terminal. The former is based on the Android operating system and is designed for voice recognition control; the latter uses a ZigBee microcontroller to achieve control and feedback. This article uses the built-in voice control system of the smart home to subtly replace the conventional control method of the smart home. Moreover, the dialogue with users is very high, easy to use, low cost, and very practical. In addition, the research methods used in this article can also be applied to other related fields.

Review on Biomass Derived Activated Carbons as Electrochemical Electrode Material for Supercapacitor Device

Abstract: To face the challenge of the finite nature of fossil fuels and large energy crises across the globe, there is an urgent requirement for sustainable and renewable energy sources. Moreover, it is essential to focus on energy storage in order to meet the demand of future generations. Among various energy storage devices such as fuel cells, batteries, capacitors, supercapacitors, flywheels, etc., it is the supercapacitor device that has elicited extensive research interest recently because of prominent features like high power density, fast recharge capability, and long cycle life. The main objective of this article is to review the enhancement of the electrochemical performance of supercapacitor devices. The electrochemical properties of the supercapacitor device majorly depend on the electrode materials used, which include carbonaceous materials, metal oxides, and conducting polymers. In order to reduce energy shortages and environmental pressure, carbon materials derived from biomass/waste materials have been considered remarkable candidates for electrode materials with the advantages of high abundance, low cost, and environmental friendliness. This review shows the complied study of various methodologies for the preparation of activated carbons derived from different biomass residues such as plants, animals, and microorganisms, which have been investigated in the past few years as electrochemical electrode materials for supercapacitors. Further, ongoing challenges and potential improvements in this area for creating efficient energy storage devices are also discussed. The goal of this review article is to aid in the creation of new insights for energy storage applications of biomass-generated carbons that will lead to sustainable energy development.

A facile and efficient voltammetric sensor for marbofloxacin detection based on zirconium-based metal-organic framework UiO-66/nitrogen-doped graphene nanocomposite

Improper and persistent use of marbofloxacin (MAR) is prone to produce residues that deteriorate the surrounding environment and endanger our health. Hence, it is urgent to stablish a facile and efficient detection technique for the detection of MAR. Herein, a highly efficient electrochemical platform was constructed for sensitive determination of MAR using zirconium-based metal–organic framework (UiO-66)/nitrogen-doped graphene (NG) nanocomposites (UiO-66/NG). The surface morphology, crystalline phase elemental composition, and electrochemical behavior of UiO-66/NG were characterized by various spectroscopic and electrochemical techniques. Owing to the synergistic interaction of UiO-66 MOFs and NG nanosheets, the UiO-66/NG exhibited remarkable catalytic activity for MAR oxidation. As consequently, the UiO-66/NG demonstrated a wide linear response range (0.01 − 10 μM), high sensitivity (1.80 μM μA−1 cm−2) and low detection limit (0.002 μM). The proposed UiO-66/NG/GCE demonstrated robust voltammetric responses even in the presence of excess potential interfering species and retained stable response signals for at least two weeks. More importantly, the proposed UiO-66/NG/GCE displayed highly consistent sensitivity toward MAR detection in both aquaculture water matrices and PBS buffer, and achieved accurate and sensitive detection of MAR with good recovery. Together with its low cost and facile operation, the UiO-66/NG/GCE shows great prospects for trace detection of MAR from complex matrices.

Transformative Peer Connections: Early Experiences From the ASCO Palliative Care Community of Practice.

The integration of palliative care into routine oncology practice is the standard of care by most leading cancer organizations. Palliative medicine helps to deliver higher quality of care at a lower cost. However, there are barriers to implementing palliative oncology at many institutions for myriad reasons. In this article, we discuss an innovative strategy that ASCO implemented called the Communities of Practice (CoP). We share our experiences as the Palliative Care CoP and how our group seeks to develop processes and structures to collectively promote systemic change and enhance palliative care delivery for people with cancer. Our Palliative Care CoP engages with senior leaders, administrators, and those in power to achieve a shared vision of delivering holistic health care for people with serious illness. We continue to evolve to meet our members' growing needs by addressing eight main domains: (1) increasing palliative care education and resources; (2) creating opportunities for global palliative care research; (3) providing peer mentorship and community building; (4) engaging with patient advocates; (5) supporting and developing interdisciplinary teams; (6) assisting with professional development and identity formation, especially for trainees and early career faculty; (7) extending our outreach through social media; and (8) enhancing the clinical practice of palliative oncology. The ASCO CoP has been a vital forum to realize ASCO's mission of conquering cancer and advancing the Art and Science of Cancer Care: From Comfort to Cure.

A New Sufficient & Necessary Condition for Testing Linear Separability Between Two Sets.

As a fundamental mathematical problem in the field of machine learning, the linear separability test still lacks a theoretically complete and computationally efficient method. This paper proposes and proves a sufficient and necessary condition for linear separability test based on a sphere model. The advantage of this test method is two-fold: (1) it provides not only a qualitative test of linear separability but also a quantitative analysis of the separability of linear separable instances; (2) it has low time cost and is more efficient than existing test methods. The proposed method is validated through a large number of experiments on benchmark datasets and artificial datasets, demonstrating both its correctness and efficiency.

HPETC: History Priority Enhanced Tensor Completion for Network Distance Measurement

In network distance measurement, how to estimate the whole network distance data from partially observed samples has attracted lots of attention because of its significance for network performance evaluation. Matrix completion becomes the most effective approach. However, the two-dimension matrix can only capture the spatial features in the network distance data while ignoring the temporal features. To conquer the problem, few recent studies begin to model the network distance data as a three-dimension tensor and propose tensor completion approaches for distance estimation. Although promising, existing tensor completion approaches still suffer the problem of low recovery accuracy and high measurement cost because they ignore the history priority information. To fully utilize both spatial and temporal features hidden in the distance data, this paper formulates a novel History Priority Enhanced Tensor Completion (HPETC) for distance estimation as a weighted tensor nuclear norm minimization problem where the weight is defined based on the history subspaces information. To solve the weighted tensor nuclear norm minimization problem, we firstly transform it into a factorization-based Frobenius norm minimization problem to avoid costly T-SVD computations, and then propose an iterative algorithm to solve the transformed problem. We further derive a theoretical sampling bound that is lower than the existing sampling bound, thus leads a lower measurement cost. We demonstrate the effectiveness of the proposed algorithm by conducting extensive experiments using two real network distance datasets. The result shows that the proposed algorithm can not only improve the estimation accuracy but also reduce the sampling complexity compared to the state-of-the-art approaches.

Mutual annotation-based prediction of protein domain functions with Domain2GO.

Identifying unknown functional properties of proteins is essential for understanding their roles in both health and disease states. The domain composition of a protein can reveal critical information in this context, as domains are structural and functional units that dictate how the protein should act at the molecular level. The expensive and time-consuming nature of wet-lab experimental approaches prompted researchers to develop computational strategies for predicting the functions of proteins. In this study, we proposed a new method called Domain2GO that infers associations between protein domains and function-defining gene ontology (GO) terms, thus redefining the problem as domain function prediction. Domain2GO uses documented protein-level GO annotations together with proteins' domain annotations. Co-annotation patterns of domains and GO terms in the same proteins are examined using statistical resampling to obtain reliable associations. As a use-case study, we evaluated the biological relevance of examples selected from the Domain2GO-generated domain-GO term mappings via literature review. Then, we applied Domain2GO to predict unknown protein functions by propagating domain-associated GO terms to proteins annotated with these domains. For function prediction performance evaluation and comparison against other methods, we employed Critical Assessment of Function Annotation 3 (CAFA3) challenge datasets. The results demonstrated the high potential of Domain2GO, particularly for predicting molecular function and biological process terms, along with advantages such as producing interpretable results and having an exceptionally low computational cost. The approach presented here can be extended to other ontologies and biological entities to investigate unknown relationships in complex and large-scale biological data. The source code, datasets, results, and user instructions for Domain2GO are available at https://github.com/HUBioDataLab/Domain2GO. Additionally, we offer a user-friendly online tool at https://huggingface.co/spaces/HUBioDataLab/Domain2GO, which simplifies the prediction of functions of previously unannotated proteins solely using amino acid sequences.

Ceftaroline fosamil treatment patterns and outcomes in adults with community-acquired pneumonia: a real-world multinational, retrospective study.

This multicentre, observational, retrospective chart review study assessed ceftaroline fosamil treatment patterns and outcomes in adults hospitalized with community-acquired pneumonia (CAP) in usual care settings. Anonymized patient data were extracted from hospital records of adults with CAP who received ≥4 consecutive IV ceftaroline fosamil doses at sites in Brazil, Colombia, France, Greece, Italy, Russia and Spain. The dataset included 185 patients (58.9% male; mean age 62.2 years), of whom 128 (69.2%) had severe CAP defined by CURB-65, PSI/PORT score or physician judgement. Streptococcus pneumoniae (n = 44; 23.8%) and Staphylococcus aureus [MSSA (n = 15) and MRSA (n = 14)] were the most frequently identified pathogens. Clinical response occurred in 151 (81.6%) patients overall, and in 104 (81.3%) severe CAP patients. Response within ≤4 and >4 days occurred in 79 (42.7%) and 62 (33.5%) patients (unknown, n = 10), respectively. Twenty (10.8%) patients required readmission within 30 days. Thirty-day all-cause mortality rates were 9.7% (n = 18) overall and 10.2% (n = 13) in severe CAP. In sensitivity analysis using ICU admission as a more objective marker of severe CAP (n = 75), clinical response and 30 day mortality occurred in 57 (76.0%) and 10 (13.3%) patients, respectively. Overall, clinical response to ceftaroline fosamil was associated with >60% shorter length of ICU stay (3.6 versus 9.2 days), and >30% lower hospital costs ($8449 versus $12 559) versus non-responders. Ceftaroline fosamil was effective in treating adults with CAP, including severe CAP, in Europe and Latin America. Clinical response to ceftaroline fosamil was associated with reductions in healthcare resource use compared with non-responders.

Structural optimization of serpentine channel water-cooled plate for lithium-ion battery modules based on multi-objective Bayesian optimization algorithm

Maintaining the battery within its optimal operating temperature range and preventing thermal runaway is crucial. Serpentine channel water-cooled plate (SCWCP) has been widely employed in battery pack cooling. The challenge lies in enhancing the cooling efficiency of SCWCP while minimizing energy consumption. Due to the high efficiency and robustness of the multi-objective Bayesian optimization (MOBO), it is employed to systematically optimize the SCWCP for lithium batteries. The width, depth, and turning radius of the serpentine flow channels are optimized to minimize both the maximum battery module temperature (Tmax) and the pumping power (PP) of the SCWCP. The MOBO process integrates structural parameter adjustments into Computational Fluid Dynamics (CFD) simulations through an automated iterative approach. Subsequently, a Pareto front is generated based on Tmax and PP, and the K-means clustering algorithm identifies four design solutions with different performance orientations. Compared with the initial design, Tmax of the optimal design decreases slightly, but with a reduction in PP of 71 %. Compared to other evolutionary algorithms, MOBO algorithm exhibits superior computational efficiency in providing an optimal design solution set at a lower computational cost.

A new identification of Lagrange multipliers to study solutions of nonlinear Caputo–Fabrizio fractional problems

This research aims to provide a new identification of Lagrange multipliers using a novel hybrid method based on the Khalouta transform method and the variational iteration method and to study the solutions of nonlinear fractional problems. This method is called Khalouta variational iteration method (KHVIM). The fractional derivative is considered in Caputo–Fabrizio sense. The uniqueness and convergence results are investigated using Banach fixed point theorem. The results are obtained in the form of successive approximations corresponding to the proposed problem. To demonstrate the accuracy and effectiveness of the proposed method, three different nonlinear fractional partial differential equations are provided. Approximate solutions of the fractional equations were obtained. These solutions quickly converged to exact solutions with lower computational cost. Furthermore, the method used in this study is more generalized and allows our results to be more extensive and cover several new and existing fractional problems in the literature.

In-situ capture defects through molecule grafting assisted in coal-based hard carbon anode for sodium-ion batteries

Sodium-ion batteries is not essential but also potential to support energy storage system due to the low cost. It's worth noting that low price and high carbon production helps coal extraordinary promise as hard carbon precursor. However, low initial coulombic efficiency (ICE) and practical Na+ storage specific capacity barrier their application owing to lots of defects. Herein, a grafting method is proposed to decrease defects, introducing glucose with simple one step carbonization process. Utilizing the reaction between glucose and coal reduces the defects, which generates a high ICE with less irreversible sodium ion consumption. Meanwhile the expanded carbon layer spaces enable the fast sodium ions transfer, leading to an optimized sodium storage performance. The grafted glucose obviously decreases the defects during the carbonization process and contributes extra sodium storage capacity. The optimal CG0.1 exhibits higher reversible capacity of 293 mAh∙g−1 and ICE of 84 % than pristine coal pyrolytic carbon (250 mAh∙g−1 and 79 %). Besides, it also delivers outstanding capacity retention with 95.3 % and 95.8 % after 1000 cycles at 0.3 A∙g−1 (240 mAh∙g−1) and at 1 A∙g−1 (207 mAh∙g−1)

The Discovery and Development of Glucagon-Like Peptide-1 Receptor Agonists.

Diabetes mellitus has become a serious life-threatening disease. As one of the new drugs for the treatment of diabetes, GLP-1 receptor agonists have attracted a lot of attention. Compared with traditional hypoglycemic drugs, GLP-1 receptor agonists have good safety and tolerability. To a certain extent, they overcome the problem of the short half-life of natural GLP-1 in vivo and can exist stably in patients for a long time, achieving good results in the treatment of diabetes, as well as improving the symptoms of some complications. The GLP-1 receptor agonists in the market are all peptide drugs. Compared with peptide drugs, small molecule agonists have the advantages of low cost and oral administration. In this article, we review the recent research progress of GLP-1 receptor agonists.

Biomass ash waste from agricultural residues: Characterisation, reactivity and potential to develop one-part geopolymer cement

Biomass is burned to generate heat and electricity. The use of biomass fuel has acquired great importance in the last years owing to its carbon-neutral nature. As a result, there is a massive build-up of biomass ash that will keep growing for decades to come. This study uses biomass ash waste to fabricate cements for construction and it presents a “zero fossil energy consumption and zero waste generation" approach to cement production. We measure the reactivity of crop biomass ash (CBA) from a power plant and the properties of one-part, CBA-slag geopolymer cements made with it.CBA has pozzolanic reactivity and potential as precursor for geopolymer fabrication. Up to 30% of CBA can be blended with ground granulated blast furnace slag (GGBS) to produce sound, one-part geopolymers activated with sodium carbonate and/or sodium silicate. Beyond this ash threshold, the geopolymer matrix experiences a strength reduction due to insufficient cement content. A one-part geopolymer consisting of 30% CBA and 70% GGBS activated with 10% sodium carbonate achieved a compressive strength of 30.70MPa at 28 days of curing at ambient temperature. However, it exhibited a prolonged setting, taking up to 5hours to initially set and 8hours to finally set. Adding a sodium silicate activator significantly improved mechanical performance and accelerated setting. A formulation activated with 20% sodium silicate and 5% sodium carbonate reached a compressive strength of 46.09MPa at 28 days, with setting times reduced to 57minutes for the initial setting and 1hour 15minutes for the final setting. The sodium silicate activator enhanced hydrate formation and facilitated polymerization thereby accelerating the set and enhancing mechanical performance.Owing to the low carbon emission factor of CBA, the one-part CBA-GGBS has a low carbon footprint, which can reduce carbon emission by 80% in comparison to PC-GGBS (CEM II) mortar with a similar compressive strength. The CBA-GGBS geopolymer is economically feasible, applicable, and market attractive due to its low cost compared with equivalent PC products.