• Energy Research
• 2021-2025close
• engineering and technology close
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Abstract With the rapid advancement in wearable electronics, energy harvesting devices based on triboelectric nanogenerators (TENGs) have been intensively investigated for providing sustainable power supply for them. However, the fabrication of wearable TENGs still remains great challenges, such as flexibility, breathability and washability. Here, a route to develop a new kind of woven-structured triboelectric nanogenerator (WS-TENG) with a facile, low-cost, and scalable electrospinning technique is reported. The WS-TENG is fabricated with commercial stainless-steel yarns wrapped by electrospun polyamide 66 nanofiber and poly(vinylidenefluoride-co-trifluoroethylene) nanofiber, respectively. Triggered by diversified friction materials under a working principle of freestanding mode, the open-circuit voltage, short-circuit current and maximum instantaneous power density from the WS-TENG can reach up to 166 V, 8.5 µA and 93 mW/m2, respectively. By virtue of high flexibility, desirable breathability, washability and excellent durability, the fabricated WS-TENG is demonstrated to be a reliable power textile to light up 58 light-emitting diodes (LED) connected serially, charge commercial capacitors and drive portable electronics. A smart glove with stitched WS-TENGs is made to detect finger motion in different circumstances. The work presents a new approach for self-powered textiles with potential applications in biomechanical energy harvesting, wearable electronics and human motion monitoring.

This study focuses on formulating the most sustainable concrete by incorporating recycled concrete aggregates and other products retrieved from construction and demolition (C&D) activities. Both recycled coarse aggregates (RCA) and recycled fine aggregates (RFA) are firstly used to fully replace the natural coarse and fine aggregates in the concrete mix design. Later, the cement rich ultrafine particles, recycled glass powder and mineral fibres recovered from construction and demolition wastes (CDW) are further incorporated at a smaller rate either as cement substituent or as supplementary additives. Remarkable properties are noticed when the RCA (4–12 mm) and RFA (0.25–4 mm) are fully used to replace the natural aggregates in a new concrete mix. The addition of recycled cement rich ultrafines (RCU), Recycled glass ultrafines (RGU) and recycled mineral fibres (RMF) into recycled concrete improves the modulus of elasticity. The final concrete, which comprises more than 75% (wt.) of recycled components/materials, is believed to be the most sustainable and green concrete mix. Mechanical properties and durability of this concrete have been studied and found to be within acceptable limits, indicating the potential of recycled aggregates and other CDW components in shaping sustainable and circular construction practices. The authors wish to acknowledge the financial support from EU Horizon 2020 Project VEEP ‘‘Cost-Effective Recycling of C&DW in High Added Value Energy Efficient Prefabricated Concrete Compo-nents for Massive Retrofitting of our Built Environment” (No.723582).

Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System.

Bridges in Ukraine are one of the most important components of the infrastructure, requiring attention from government agencies and constant funding. The object of the study was the methodology for quantifying the condition of bridge components. The Artificial Neural Network-based (ANN) tool was developed to quantify the technical condition of bridge components. The literature analysis showed that in most cases the datasets were obtained during the inspection of bridges to solve the problems of assessing the current technical condition. The lack of such a database prompted the creation of a dataset on the basis of the Classification Tables of the Operating Conditions of the Bridge Components (CT). Based on CTs, five datasets were formed to assess the condition of the bridge components: bridge span, bridge deck, pier caps beam, piers and abutments, approaches. The next step of this study was creating, training, validating and testing ANN models. The network with ADAM loss function and softmax activation showed the best results. The optimal values of MAPE and R2 were achieved at the 100th epoch with 64 neurons in the hidden layer and were equal to 0.1% and 0.99998, respectively. The practical application of the ANN models was carried out on the most common type of bridge in Ukraine, namely, a road beam bridge of small length, made of precast concrete. The novelty of this study consists of the development of a tool based on the use of ANN model, and the proposal to modify the methodology for quantifying the condition of bridge components. This will allow minimizing the uncertainties associated with the subjective judgments of experts, as well as increasing the accuracy of the assessment.

Bioplastics are alternatives of conventional petroleum-based plastics. Bioplastics are polymers processed from renewable sources and are biodegradable. This study aims at conducting an environmental impact assessment of the bioprocessing of agricultural wastes into bioplastics compared to petro-plastics using an LCA approach. Bioplastics were produced from potato peels in laboratory. In a biochemical reaction under heating, starch was extracted from peels and glycerin, vinegar and water were added with a range of different ratios, which resulted in producing different samples of bio-based plastics. Nevertheless, the environmental impact of the bioplastics production process was evaluated and compared to petro-plastics. A life cycle analysis of bioplastics produced in laboratory and petro-plastics was conducted. The results are presented in the form of global warming potential, and other environmental impacts including acidification potential, eutrophication potential, freshwater ecotoxicity potential, human toxicity potential, and ozone layer depletion of producing bioplastics are compared to petro-plastics. The results show that the greenhouse gases (GHG) emissions, through the different experiments to produce bioplastics, range between 0.354 and 0.623 kg CO2 eq. per kg bioplastic compared to 2.37 kg CO2 eq. per kg polypropylene as a petro-plastic. The results also showed that there are no significant potential effects for the bioplastics produced from potato peels on different environmental impacts in comparison with poly-β-hydroxybutyric acid and polypropylene. Thus, the bioplastics produced from agricultural wastes can be manufactured in industrial scale to reduce the dependence on petroleum-based plastics. This in turn will mitigate GHG emissions and reduce the negative environmental impacts on climate change.

Micro- and nanoscale patterned monolayers of plasmonic nanoparticles were fabricated by combining concepts from colloidal chemistry, self-assembly, and subtractive soft lithography. Leveraging chemical interactions between the capping ligands of pre-synthesized gold colloids and a polydimethylsiloxane stamp, we demonstrated patterning gold nanoparticles over centimeter-scale areas with a variety of micro- and nanoscale geometries, including islands, lines, and chiral structures (e.g., square spirals). By successfully achieving nanoscale manipulation over a wide range of substrates and patterns, we establish a powerful and straightforward strategy, nanoparticle chemical lift-off lithography (NP-CLL), for the economical and scalable fabrication of functional plasmonic materials with colloidal nanoparticles as building blocks, offering a transformative solution for designing next-generation plasmonic technologies.