Waste metal for improving concrete performance and utilisation as an alternative of reinforcement bar

Waste material disposal is considered as a difficult issue to adopt in current world. Waste metal, which has been recognised as a major problem in the environment and resource deficiency, could have important implications in the concrete construction industries. Waste metal utilisation in construction of reinforced cement concrete (RCC) works is immerging in recent time. Construction industries are looking for cost effective structural materials and utilisation of renewable materials. Metal waste such as chips of tin, still and other metal fragments which is abandoned and spread in the environment could be utilize as a replacement of traditional steel reinforcement bar in the RCC. In this experiment, three different types of waste metal have been compared with commercial 40, 60 and 72 graded steel reinforcement bar. Compressive strength class of C25 concrete was used in the experiment and mechanical properties of concrete incorporating different waste metal were investigated in the first stage. Finally, three-point bend test on short beam was performed to compare their performances. Smaller metal fragments has shown better performance through micro crack bridging in concrete during loading stage and hence better than ordinary reinforcement concrete structure in some extent

Impact of graphene oxide and highly reduced graphene oxide on cement based composites

This study examines and compares the performance of two specific forms of graphene nanomaterials in the cement based composite, namely graphene oxide (GO) and reduced graphene oxide (rGO). A typical forms of GO with the average C:O ratio of 54:46 and a rGO with the average C:O ratio of 82:18 were used in the cement based paste composites. rGO was treated with superplasticizer to improve its dispersibility in water. Both GO and rGO were used as 0.02, 0.04, 0.06 wt% of cement. The effect of GO and rGO on workability, early age hydration, microstructure, mechanical and transport properties was determined. Different characteristics of GO and rGO such as molecular structure, functional groups, d spacing, size and physical strength influenced the properties of the cement based composites. The workability and final setting time of composite gradually decreased compared to 100% PC (control) with higher dosages of GO up to 0.06 wt% (of cement), which is due to the dominant oxygen functional groups and the hydrophilic nature of GO. To the contrary, the workability and final setting time increased in the rGO composites compared to the control mix due to the almost hydrophobic nature of rGO and the presence of superplasticiser. The XRD and TGA quantification of the hydration products shows that GO composites have a greater content of Ca(OH)2 and C-S-H compared to rGO composites measured at 1, 7 and 28 days. Micropores (smaller than ∼10 µm) in GO composites were observed to be filled with calcium silicate hydrate (C-S-H) gel and crystalline compounds. Random pore filling nature was observed in rGO composites and ettringite was more common element in those pores. Meso and gel pores

Self-Healing Concrete and Cementitious Materials

Concrete is one of the most used materials in the world with robust applications and increasing demand. Despite considerable advancement in concrete and cementitious materials over last centuries, infrastructure built in the present world with these materials, such as dams, roads, bridges, tunnels and buildings requires intensive repair and maintenance throughout its design life. Self-healing concrete and cementitious materials, which have the ability to recover after initial damage, have the potential to address these challenges. Self-healing technology in concrete and cementitious materials can mitigate the unnecessary repair and maintenance of built infrastructure as well as overall CO2 emission due to cement production. This chapter provides the state-of-the-art of self-healing concrete and cementitious materials, mainly focusing on autogenic or intrinsic self-healing using fibre, shrinkable polymers, minerals and supplementary cementitious materials, and autonomic self-healing using non-traditional concrete materials such as microscale to macroscale capsule as well as vascular systems with polymeric, mineral and bacterial agents

Concrete with Organic Waste Materials as Aggregate Replacement

The disposal of high volumes of organic waste is a global issue. Using organic waste instead of sand as an aggregate material for concrete could reduce the strain on waste treatment processes and on the extraction of finite resources. At the same time, it could be a climate change mitigation strategy, by storing the biogenic carbon contained in the organic waste. This project investigated the viability of replacing 10% of fine aggregate in concrete with various organic waste materials, namely rice husk ash, wood ash, corncob granules, and wheat straw. The fresh concrete’s properties were studied using the slump test, and the hardened concrete’s mechanical properties were measured using the compressive strength and flexural strength tests. In this study, 14 days of curing were considered for the mechanical tests, although the 28-day mechanical strength is more generally accepted. The mechanical performances along with a life cycle assessment (LCA) comparison between the concrete with organic waste and traditional concrete were conducted. The results suggested that rice husk ash and wood ash are the most-suitable organic waste products for use as aggregate replacers considering the mechanical properties. The concrete samples incorporating wheat straw and corncob granules exhibited relatively low strength; unless advanced treatment methods are applied to enhance the concrete’s performance, the utilization of these organic wastes in concrete may be limited. The environmental impact assessment of traditional concrete shows that the main contributor to almost every impact category is the production of Portland cement. Sand production contributes only marginally to the overall impact of the concrete. In terms of life-cycle greenhouse gas (GHG) emissions, traditional concrete exhibits the lowest GWP impact per cubic meter when mechanical properties are included in the functional unit used for the comparison. Nevertheless, concrete samples with wood ash and rice husk ash partially offset their lower compressive strength with higher carbon sequestration, showing a similar GWP impact to traditional concrete. This makes them promising alternatives, especially for cases where limited compressive strengths are needed. Further investigations to improve their mechanical properties and optimize their performance are warranted

Application of graphene in fiber-reinforced cementitious composites: A review

Graphene with fascinating properties has been deemed as an excellent reinforcement for cementitious composites, enabling construction materials to be smarter, stronger, and more durable. However, some challenges such as dispersion issues and high costs, hinder the direct incorporation of graphene-based reinforcement fillers into cementitious composites for industrial production. The combination of graphene with conventional fibers to reinforce cement hence appears as a more promising pathway especially towards the commercialization of graphene for cementitious materials. In this review paper, a critical and synthetical overview on recent research findings of the implementation of graphene in fiber-reinforced cementitious composites was conducted. The preparation and characterization methods of hybrid graphene-fiber fillers are first introduced. Mechanical reinforcing mechanisms are subsequently summarized, highlighting the main contribution of nucleation effect, filling effect, interfacial bonding effect, and toughening effect. The review further presents in detail the enhancements of multifunctional properties of graphene-fiber reinforced cementitious composites, involving the interfacial properties, mechanical properties, durability, electrical conductivity, and electromagnetic interference shielding. The main challenges and future prospects are finally discussed to provide constructive ideas and guidance to assist with relevant studies in future

Digital Predistortion Based Experimental Evaluation of Optimized Recurrent Neural Network for 5G Analog Radio Over Fiber Links

In the context of Enhanced Remote Area Communications (ERAC), Radio over Fiber (RoF) technology plays a crucial role in extending reliable connectivity to underserved and remote areas. This paper explores the significance of fifth-generation (5G) Digital Predistortion (DPD) role in mitigating non-linearities in Radio over Fiber (RoF) systems for enhancing communication capabilities in remote regions. The seamless integration of RoF and 5G technologies requires robust linearization techniques to ensure high-quality signal transmission. In this paper, we propose and exhibit the effectiveness of a machine learning (ML)-based DPD method for linearizing next-generation Analog Radio over Fiber (A-RoF) links within the 5G landscape. The study investigates the use of an optimized recurrent neural network (ORNN) based DPD experimentally on a multiband 5G new radio (NR) A-RoF system while maintaining low complexity. The ORNN model is evaluated using flexible-waveform signals at 2.14 GHz and 5G NR signals at 10 GHz transmitted over a 10 km fiber length. The proposed ORNN-based machine learning approach is optimized and is compared with conventional generalized memory polynomial (GMP) model and canonical piecewise linearization (CPWL) methods in terms of Adjacent Channel Power Ratio (ACPR), Error Vector Magnitude (EVM), and in terms of computation complexity including, storage, time and memory consumption. The findings demonstrate that the proposed ORNN model reduces EVM to below 2% as compared to 12% for non-compensated cases while ACPR is reduced by 18 dBc, meeting 3GPP limits

Nano-cement composite with graphene oxide produced from epigenetic graphite deposit

This study presents the development of a nano-cement composite with graphene oxide (GO) carbon-based nanomaterials synthesized from a high-purity epigenetic graphite deposit. Diamond drill sampled graphite mineralization was upgraded through beneficiation and purification to recover a high-purity graphite product (99.9% graphitic carbon “Cg”). An alternate and improved chemical oxidation process based on the Modified Hummers method was adopted for the synthesis of GO from high-purity graphite. Microstructural analysis were performed to characterise GO. The GO consists of single bondOH, single bondC=O, single bondCOOH, and C-O-C functional groups with a layer thickness of 1.2 nm, 2 to 3 layers of graphene, an interlayer distance of 0.90 nm and a Raman (ID/IG) ratio of 0.79. The effect of 0.02, 0.04, and 0.06 wt% GO of cement on the composite workability, hydration, microstructure, mechanical and transport properties was determined. Increasing the concentration of GO in the composite decreased the workability due to the hydrophilic nature of the 2D planar surface. The rate of hydration accelerated and the cumulative hydration heat increased with the increasing proportions of GO in the composite. GO dosages about 0.02 and 0.04 wt% of cement in the composites resulted the maximum enhancement of compressive and flexural strength by 83 and 26%, respectively, compared to the control mix (0 wt% GO). The microstructural investigation shows that GO enhanced the hydration of calcium hydroxide (CH) and calcium silicate hydrate (C-S-H) during the nucleation and growth stages, filled pores, bridged micro-cracks and created interlocking between the cement hydration products. Collectively, these effects ultimately improved the mechanical properties of the composites. Also, in this process, the 0.02 and 0.04 wt% GO cement composite increased the electrical resistivity by 11.5%, and decreased the sorptivity by 29%, respectively, both of which improved the overall performance of the composite

AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.  Other InformationPublished in:HeliyonLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://doi.org/10.1016/j.heliyon.2023.e17575 </p

Electrochemical Biosensing and Deep Learning-Based Approaches in the Diagnosis of COVID-19: A Review

COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and caused life-threatening medical complications and elevated mortality rates, especially among older adults and people with existing morbidity. Current evidence suggests that the virus spreads primarily through respiratory droplets emitted by infected persons when breathing, coughing, sneezing, or speaking. These droplets can reach another person through their mouth, nose, or eyes, resulting in infection. The gold standard\u27\u27 for clinical diagnosis of SARS-CoV-2 is the laboratory-based nucleic acid amplification test, which includes the reverse transcription-polymerase chain reaction (RT-PCR) test on nasopharyngeal swab samples. The main concerns with this type of test are the relatively high cost, long processing time, and considerable false-positive or false-negative results. Alternative approaches have been suggested to detect the SARS-CoV-2 virus so that those infected and the people they have been in contact with can be quickly isolated to break the transmission chains and hopefully, control the pandemic. These alternative approaches include electrochemical biosensing and deep learning. In this review, we discuss the current state-of-the-art technology used in both fields for public health surveillance of SARS-CoV-2 and present a comparison of both methods in terms of cost, sampling, timing, accuracy, instrument complexity, global accessibility, feasibility, and adaptability to mutations. Finally, we discuss the issues and potential future research approaches for detecting the SARS-CoV-2 virus utilizing electrochemical biosensing and deep learning

Ultrasonic-assisted synthesis of graphene oxide – fungal hyphae: An efficient and reclaimable adsorbent for chromium(VI) removal from aqueous solution

In this study, a hybrid film bio-nanocomposite material was developed based on the graphene oxide/fungal hyphae (GO-FH) interaction. The developed GO-FH bio-nanocomposite material was used for the removal of hexavalent chromium from aqueous solution. The GO-FH bio-nanocomposite material was prepared by ultrasonic irradiation technique. The synthesized GO-FH bio-nanocomposite material was characterized by XRD, FT-IR, SEM, TEM and TGA. The adsorption experiments were carried out in batch mode to optimize parameters such as pH, adsorbent dosage, initial Cr(VI) ion concentration, contact time and shaking speed. The results indicated that the adsorption of Cr(VI) onto GO-FH bio-nanocomposite material was pH dependant, with the maximum adsorption capacity of 212.76 mg/g occurred at pH 2.0. The adsorption studies followed, Langmuir isotherm and pseudo second order kinetic model. Findings demonstrates that GO-FH bio-nanocomposite material exhibited excellent regeneration performance