Search Results (9)

Three-dimensional printing is promising in the pharmaceutical industry for personalized medicine, on-demand production, tailored drug loading, etc. Pressure-assisted microsyringe (PAM) printing is popular due to its low cost, simple operation, and compatibility with heat-sensitive drugs but is limited by ink formulations lacking the essential characteristics, impacting their performance. This study evaluates inks based on sodium alginate (SA), hydroxypropyl cellulose (HPC H), and hydroxypropyl methylcellulose (HPMC K100 and K4) for PAM 3D printing by analyzing their rheology. The formulations included the model drug Fenofibrate, functional excipients (e.g., mannitol, polyethylene glycol, etc.), and water or water–ethanol mixtures. Pills and thin films as an oral dosage were printed using a 410 μm nozzle, a 10 mm/s speed, a 50% infill density, and a 60 kPa pressure. Among the various formulated inks, only the ink containing 0.8% SA achieved successful prints with the desired shape fidelity, linked to its rheological properties, which were assessed using flow, amplitude sweep, and thixotropy tests. This study concludes that (i) an ink’s rheological properties—viscosity, shear thinning, viscoelasticity, modulus, flow point, recovery, etc.—have to be considered to determine whether it will print well; (ii) printability is independent of the dosage form; and (iii) the optimal inks are viscoelastic solids with specific rheological traits. This research provides insights for developing polymer-based inks for effective PAM 3D printing in pharmaceuticals. Full article

In the present study, the feasibility to achieve localized induction heating and debonding of multi-material composite structures is assessed in testing coupons prepared by Automated Fiber Placement (AFP) and extrusion-based additive manufacturing (AM) technologies. Nano-compounds of Polyether-ketone-ketone (PEKK) with iron oxide nanoparticles acting as electromagnetic susceptors have been processed in a parallel co-rotating twin-screw extruder to produce filament feedstock for extrusion-based AM. The integration of nanocomposite interlayers as discrete debonding zones (DZ) by AFP-AM manufacturing has been investigated for two types of sandwich-structured laminate composites, i.e., laminate-DZ-laminate panels (Type I) and laminate-DZ-AM gyroid structures (Type II). Specimens were exposed to an alternating magnetic field generated by a radio frequency generator and a flat spiral copper induction coil, and induction heating parameters (frequency, power, heating time, sample standoff distance from coil) have been investigated in correlation with real-time thermal imaging to define the debonding process window without compromising laminate quality. For the optimized process parameters, i.e., 2–3 kW generator power and 20–25 mm standoff distance, corresponding to magnetic field intensities in the range of 3–5 kA m⁻¹, specimens were effectively heated above PEKK melting temperature, exhibiting high heating rates within the range of 5.3–9.4 °C/s (Type I) and 8.0–17.5 °C/s (Type II). The results demonstrated that localized induction heating successfully facilitated debonding, leading to full unzipping of the debonding zones in both laminate structures. Further insight on PEKK nanocomposites debonding performance was provided by thermal, morphological characterization and non-destructive inspection via X-ray micro-computed tomography at different processing stages. The developed framework aims to contribute to the development of rapid, on-demand joining, repair and disassembly technologies for thermoplastic composites, towards more efficient maintenance, repair and overhaul operations in the aviation sector and beyond. Full article

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity. Full article

In this study, the heating capacity of nanocomposite materials enhanced with magnetic nanoparticles was investigated through induction heating. Thermoplastic (TP) matrices of polypropylene (PP), thermoplastic polyurethane (TPU), polyamide (PA12), and polyetherketoneketone (PEKK) were compounded with 2.5–10 wt.% iron oxide-based magnetic nanoparticles (MNPs) using a twin-screw extrusion system. Disk-shape specimens were prepared by 3D printing and injection molding. The heating capacity was examined as a function of exposure time, frequency, and power using a radio frequency (RF) generator with a solenoid inductor coil. All nanocomposite materials presented a temperature increase proportional to the MNPs’ concentration as a function of the exposure time in the magnetic field. The nanocomposites with a higher concentration of MNPs presented a rapid increase in temperature, resulting in polymer matrix melting in most of the trials. The operational parameters of the RF generator, such as the input power and the frequency, significantly affect the heating capacity of the specimens, higher input power, and higher frequencies and promote the rapid increase in temperature for all assessed nanocomposites, enabling induced-healing and bonding/debonding on-demand applications. Full article

The accurate prediction of building heat demand plays the critical role in refined management of heating, which is the basis for on-demand heating operation. This paper proposed a prediction model framework for building heat demand based on reinforcement learning. The environment, reward function and agent of the model were established, and experiments were carried out to verify the effectiveness and advancement of the model. Through the building heat demand prediction, the model proposed in this study can dynamically control the indoor temperature within the acceptable interval (19–23 °C). Moreover, the experimental results showed that after the model reached the primary, intermediate and advanced targets in training, the proportion of time that the indoor temperature can be controlled within the target interval (20.5–21.5 °C) was over 35%, 55% and 70%, respectively. In addition to maintaining indoor temperature, the model proposed in this study also achieved on-demand heating operation. The model achieving the advanced target, which had the best indoor temperature control performance, only had a supply–demand error of 4.56%. Full article

The refined control of heating substations is of great significance for on-demand heating provision and for the efficient operation of district heating systems (DHSs). This paper proposes an integrated control strategy for substations based on the prediction of the water-supply temperature and indoor temperature. Firstly, online sequential extreme learning machine (OS-ELM) is used to predict the water-supply temperature. Then, a linear prediction model is established to predict the indoor temperature. Finally, the integrated regulation strategy is established with the goal of minimizing operational costs, aiming at ensuring heating quality and meeting the limits of the flow rate and of the supply- and return-water temperatures. The heat-saving rate, power-saving rate and indoor-temperature satisfactory rate are introduced to evaluate the regulation effect of the proposed method. The field study results show that the performance index of operation executed with the regulation strategy proposed in this paper is 9.31%, 16.33% and 20.87% higher than that without our energy-saving regulation strategy respectively. The fluctuations in the water-supply pressure and differential pressure of the secondary network are significantly reduced, and the energy-saving effect is obvious. Full article

Software-Defined Wireless Body Area Network (WBAN)s have gained significance in emergency healthcare applications for remote patients. Prioritization of healthcare data traffic has a high influence on the congestion and delay in the WBAN routing process. Currently, the energy constraints, packet loss, retransmission delay and increased sensor heat are pivotal research challenges in WBAN. These challenges also degrade the network lifetime and create serious issues for critical health data transmission. In this context, a Priority-based Energy-efficient, Delay and Temperature Aware Routing Algorithm (PEDTARA) is presented in this paper using a hybrid optimization algorithm of Multi-objective Genetic Chaotic Spider Monkey Optimization (MGCSMO). This proposed optimized routing algorithm is designed by incorporating the benefits of chaotic and genetic operators to the position updating function of enhanced Spider Monkey Optimization. For the prioritized routing process, initially, the patient data transmission in the WBAN is categorized into normal, on-demand and emergency data transmissions. Each category is ensured with efficient routing using the three different strategies of the suggested PEDTARA. PEDTARA performs optimal shortest path routing for normal data, energy-efficient emergency routing for high priority critical data and faster but priority verified routing for on-demand data. Thus, the proposed PEDTARA ensures energy-efficient, congestion-controlled and delay and temperature aware routing at any given period of health monitoring. Experiments were performed over a high-performance simulation scenario and the evaluation results showed that the proposed PEDTARA performs efficient routing better than the traditional approaches in terms of energy, temperature, delay, congestion and network lifetime. Full article

Heating, ventilation, and air-conditioning (HVAC) systems are omnipresent in modern buildings and are responsible for a considerable share of consumed energy and the electricity bill in buildings. On the other hand, solar energy is abundant and could be used to support the building HVAC system through cogeneration of electricity and heat. Micro-scale concentrated solar power (MicroCSP) is a propitious solution for such applications that can be integrated into the building HVAC system to optimally provide both electricity and heat, on-demand via application of optimal control techniques. The use of thermal energy storage (TES) in MicroCSP adds dispatching capabilities to the MicroCSP energy production that will assist in optimal energy management in buildings. This work presents a review of the existing contributions on the combination of MicroCSP and HVAC systems in buildings and how it compares to other thermal-assisted HVAC applications. Different topologies and architectures for the integration of MicroCSP and building HVAC systems are proposed, and the components of standard MicroCSP systems with their control-oriented models are explained. Furthermore, this paper details the different control strategies to optimally manage the energy flow, both electrical and thermal, from the solar field to the building HVAC system to minimize energy consumption and/or operational cost. Full article

Tearable and fillable implants are used to facilitate surgery. The use of implants that can generate heat and release a drug in response to an exogenous trigger, such as an alternating magnetic field (AMF), can facilitate on-demand combined thermal treatment and chemotherapy via remote operation. In this study, we fabricated tearable sponges composed of collagen, magnetite nanoparticles, and anticancer drugs. Crosslinking of the sponges by heating for 6 h completely suppressed undesirable drug release in saline at 37 °C but allowed drug release at 45 °C. The sponges generated heat immediately after AMF application and raised the cell culture medium temperature from 37 to 45 °C within 15 min. Heat generation was controlled by switching the AMF on and off. Furthermore, in response to heat generation, drug release from the sponges could be induced and moderated. Thus, remote-controlled heat generation and drug release were achieved by switching the AMF on and off. The sponges destroyed tumor cells when AMF was applied for 15 min but not when AMF was absent. The tearing and filling properties of the sponges may be useful for the surgical repair of bone and tissue defects. Moreover, these sponges, along with AMF application, can facilitate combined thermal therapy and chemotherapy. Full article