Politecnio die Bari - Catalogo di prodotti della Ricerca
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Enhancing 3-D-Printed Piezoresistive Sensors: An Investigation Into Process Parameters, Sensor Geometries and Materials Selection
No full textMaterial extrusion (MEX) additive manufacturing of electrically conductive polymers is an inexpensive method to fabricate piezoresistive-based sensors deployable in many fields such as biomedical, and soft robotics. Despite the clear benefits related to such a fabrication approach (i.e., zeroing assembly), 3-D printed sensors still suffer poor repeatability. This article introduces a systematic approach to studying structure-performance properties to enhance sensitivity and repeatability. We report a Gauge Factor (GF) of 6.98, which is threefold higher than traditional metal-based strain gauges, and a coefficient of variation (CV) of 1.84%, lower than any previously documented 3-D-printed sensors. The proposed approach is based on studying the impact of the main MEX variables, namely: 1) process parameters; 2) sensor geometry; and 3) substrate material. A direct correlation with layer height, number of extruded layers, and beads was found, proving that high performance in 3-D printed sensors is achieved when inter, and intra-layer voids are reduced. To prove the potentialities of the proposed high-performance sensor, the 3-D printed strain gauge was integrated within a silicone robotic gripper finger, and its performance was validated through a closed-loop proportional-integral-derivative (PID) control system that efficaciously modulated the finger's bending angle. The present research demonstrates that the optimal tuning of MEX-related parameters abruptly improves the performance of 3-D printed strain gauges, bridging the gap between MEX and real-life applications
Detectability of Potentially Colliding Space Objects via Star Trackers on at-Risk Satellites
No full textThe proliferation of objects in Low-Earth Orbit (LEO) is forcing satellites to increasingly maneuver out of potential collisions, consuming fuel and interrupting operations. Most of these burns are unnecessary, driven by conservative decisions based on limited trajectory information. Space-based observation capabilities could improve tracking accuracy, but typical onboard sensors have limited performance, making it challenging to detect faint, fast-moving objects. This paper investigates the capabilities of typical onboard electro-optical sensors to detect objects in LEO that pose a collision risk to the host platform, providing a feasibility assessment for an autonomous avoidance system. The concept is simulated in a Matlab–System Tool Kit environment over hundreds of scenarios, modeling imaging systems of varying quality, with a focus on star trackers. An observation strategy to maximize target SNR is presented. Realistic synthetic images of the scenes are generated and post-processed to derive target SNR and detection probability for each sensor. The study also assesses potential accuracy improvements from processing these observations through orbit determination (OD). Low-quality or small-aperture (2–5 cm) star trackers can only detect large-class secondary objects (>1–2 m), especially with small relative angular rates. More advanced systems achieve consistent detection and can occasionally detect objects as small as 40 cm reliably. Readout noise stands as a primary limitation for the SNR. Concerning observation timing, two favorable windows per orbit are available in about half the cases, located at opposite orbital points before closest approach. This feature efficiently constrains the orbit solution space during OD processing
Search for bosons of an extended Higgs sector in b quark final states in proton-proton collisions at = 13 TeV
No full textA Framework for the Automated and Optimal Design of Vertical Lift Modules
No full textTo this day, tasks like planning and managing warehouses remain complex. Automated storage and retrieval systems have enhanced warehousing efficiency, yet designing them optimally is still challenging, despite their importance for the efficient operation of the warehouse. This article aims to present a novel framework for automating the optimal design of vertical lift modules (VLMs), focusing on tray types, quantities, and item-tray sector assignments, based on a specified inventory list. The approach accounts for VLM's physical, manufacturing, and ergonomic constraints to ensure a manufacturable system design. To manage computational complexity, the size of the mixed-integer problem is reduced through an exact clustering of items, sectors, and layouts. The proposed framework is tested through numerical simulations using real data from an Italian VLM manufacturer
Integration of BIM and Augmented Reality for the Recovery of Historical Built Heritage: A Research Perspective
No full textPreserving historic built heritage imposes targeted interventions, combining recovery of historical structures with functional adaptation to contemporary needs without compromising their identity. The growing necessity to recover, preserve, and digitise built heritage is driving researchers and companies to explore the integration of advanced 3D modelling methodologies, such as Building Information Modeling, with immersive Augmented Reality visualisation technologies. Their integration can transform the construction sector and the recovery of architectural heritage. The motivation behind the research presented in this paper stems from the need to improve the management of recovery interventions on existing buildings, minimising risks and optimising resources. Thus, the first study’s objective is to investigate how the combined use of BIM and AR can offer efficient and precise solutions through the phases of data gathering, design, digitisation, development of immersive environment, and final product use. Then, how collaboration between academia and companies can provide advantages in solution quality, innovation, knowledge advancement, and access to advanced tools. In this framework, the paper presents the project “Augmented Reality as a decision-support tool for interventions of recovery of historic buildings” that involves Politecnico di Bari and Evholo Srl start-up (Predict SpA). Their previous experiences are detailed to highlight the feasibility of project outcomes, combining traditional and innovative methods and advanced tools to set an integrated and multidisciplinary approach to enhance and preserve historic heritage. The expected outcomes include enhanced architectural heritage conservation through improved technical operational and coordination efficiency, increased digitisation, and reduced costs and time
Multiplexed Microphotonic Ring Resonator Platform for Simultaneous Detection of SARS-CoV-2 Spike Protein and Respiratory Syncytial Virus-F Protein
No full textThe persistent burden imposed by SARS-CoV-2, respiratory syncytial virus (RSV), and other respiratory viruses highlights the demand for real-time, label-free diagnostics capable of multipathogen detection. Despite numerous proposals, existing biosensors have yet to achieve the trifecta of sub-nanomolar sensitivity, simultaneous targeting, and a truly compact footprint. Here, we show a multiplexed photonic biosensor based on cascaded microring resonators (MRRs) that can detect two different viral glycoproteins, such as the SARS-CoV-2 spike protein and the RSV-F protein, at sub-nanomolar concentrations. Three strip-waveguide rings (radii =99, 100, 101 μm; TE mode at 1310 nm) were designed via finite-element modeling and fabricated on a Si3N4-on-insulator platform using e-beam lithography and inductively coupled plasma reactive ion etching (ICP-RIE). Preliminary results demonstrate a quality factor of the order of 105 and a mean bulk sensitivity value of 150 nm/RIU, evaluated in gradually concentrated ethanol solutions flowing on the sensor surface. A common bus waveguide enables wavelength-division multiplexing (WDM). Selective, oriented immobilization of anti-spike and anti-F antibodies is achieved through a protein A interfacial layer, while the central ring is intentionally left bare as an on-chip reference. By sweeping the wavelength of a continuous wave (CW) laser across each resonance, real-time resonance shifts are monitored and quantified via Lorentzian peak fitting. By functionalizing distinct rings with viral proteins-specific antibodies, our chip achieves detection limits of approximately 0.16 nM for the SARS-CoV-2 spike protein and 0.14 nM for the RSV-F protein, matching or surpassing the performance of conventional single-target optical biosensors. Our design allows the analysis of multiple viral antigens in parallel, reducing sample volume and overall test duration. Moreover, an on-chip reference ring ensures robust signal stability by compensating for nonspecific effects, thereby enhancing accuracy and reproducibility. These findings underscore the promise of integrated photonics to reshape point-of-care diagnostics, especially in decentralized or resource-limited settings. We anticipate that this platform’s versatility can be extended to detect additional pathogens or biomolecules, providing a scalable and cost-effective tool for rapid clinical decision-making and epidemiological surveillance
Effective Comparison of Thermo-Mechanical Characteristics of Self-Compacting Concretes Through Machine Learning-Based Predictions
No full textThis present study proposes different machine learning-based predictors for the assessment of the residual compressive strength of Self-Compacting Concrete (SCC) subjected to high temperatures. The investigation is based on several literature algorithmic approaches based on Artificial Neural Networks with distinct training algorithms (Bayesian Regularization, Levenberg-Marquardt, Scaled Conjugate Gradient, and Resilient Backpropagation), Support Vector Regression, and Random Forest methods. A training database of 150 experimental data points is derived from a careful literature review, incorporating temperature (20-800 degrees C), geometric ratio (height/diameter), and corresponding compressive strength values. A statistical analysis revealed complex non-linear relationships between variables, with strong negative correlation between temperature and strength and heteroscedastic data distribution, justifying the selection of advanced machine learning techniques. Feature engineering improved model performance through the incorporation of quadratic terms, interaction variables, and cyclic transformations. The Resilient Backpropagation algorithm demonstrated superior performance with the lowest prediction errors, followed by Bayesian Regularization. Support Vector Regression achieved competitive accuracy despite its simpler architecture. Experimental validation using specimens tested up to 800 degrees C showed a good reliability of the developed systems, with prediction errors ranging from 0.33% to 23.35% across different temperature ranges
Point-of-Care Tests via Pollen-Based Nanoplasmonic Chips Combined with a Synthetic Receptor for FKBP12 Biomarker Detection at a Single-Molecule Level
No full textThe selective and rapid detection of FKBP12 is crucial due to its involvement in immunosuppression, neurodegenerative and oncological diseases, and some fundamental cellular processes. A low-cost point-of-care test (POCT) based on a simple setup, combined with plasmonic sensor chips for ultrasensitive detection of FKBP12, is developed. The sensing principle exploits pollen-based natural nanostructures covered by gold nanofilms and functionalized with synthetic GPS-SH1 receptors. The experimental results demonstrated ultrahigh performance due to the hybrid plasmonic phenomena, with a detection limit of 0.17 aM for FKBP12. This label-free optical-chemical sensor is based on portable and simple equipment, operates in 10 min, requires a small volume of the sample, and only requires a dilution step to perform the measurement on real samples. The high selectivity of the developed sensor chip for FKBP12 is demonstrated, and its applicability in complex matrices such as serum and plasma is validated. Furthermore, two surface functionalization strategies with different receptor-to-spacer ratios, 1:6 and 1:3, are investigated, identifying the optimal ratio to achieve better binding sensitivity. This work highlights the potential of plasmonic nanostructured pollen-based chips functionalized with GPS-SH1 receptors for the detection of FKBP12 at the single-molecule level, paving the way for advances in diagnostics and therapeutic monitoring via low-cost POCT with cheaper and disposable chips
Quantitative Assessment of Seismic Retrofit Strategies for RC School Buildings Using Steel Exoskeletons and Localized Strengthening
No full textThis study offers a quantitative performance assessment of integrated seismic retrofit designs applied to an in-service 1960s reinforced concrete school structure in Central Italy. The research combines in-depth experimental material characterization with complex numerical simulations in order to estimate both the independent and interaction effects of external steel exoskeletons in conjunction with localized CAM (Cucitura Attiva dei Materiali) strengthening. The experimental investigation includes extensive material characterization through core drilling and non-destructive pacometric inspections to accurately define the existing concrete properties. The numerical analysis is performed with Finite Element modeling to estimate four different structural conditions: the original state, the condition with static strengthening, the condition with additional steel exoskeletons, and the condition with both exoskeletons and localized CAM reinforcements. The results quantitatively estimate the specific performance gains from the individual retrofit strategies. The steel exoskeletons show effective reduction in inter-story drifts but negligible effect on strength-oriented failure mechanisms. Localized CAM strengthening therefore stands out as necessary in reaching adequate safety levels in all the failure mechanisms. Economic analysis reveals that while steel exoskeletons provide the major cost component, the integrated approach with localized strengthening is essential for achieving comprehensive seismic safety enhancement
Biomarkers of aging and vitality capacity
No full textThe progressive increase in the elderly population poses social and healthcare challenges, requiring the implementation of strategies aimed at effectively assessing the health status of older individuals and promoting healthy aging. The concept of healthy aging, promoted by the WHO, emphasizes the importance of extending the years of life in good health (Healthspan) rather than focusing solely on lifespan. This mini-review aims to analyze the role of biomarkers in monitoring aging and promoting healthy aging, with a focus on vitality capacity and biological resilience. Biomarkers of aging and attributes of vitality capacity, useful parameters for monitoring changes associated with the aging process, were ana-lyzed. We propose a classification of biomarkers into molecular, physiological, functional, and digital categories, further distinguishing between biomarkers related to vitality capacity and those linked to biological resilience, each with specific analytical and clinical validation criteria. Additionally, the challenges associated with validation criteria and standardization are highlighted. Despite the limitations related to their applicability, the integration of biomarkers into healthcare could revolutionize the monitoring of aging, fostering a preventive and personalized approach. This underscores the importance of their clinical application for the future of healthy aging and gerontological medicine