Experimental Analysis Research Articles

Experimental analysis of the piezomagnetic behavior of double-shear bolted connections of wire and arc additively manufactured steel

ABSTRACT In this paper, the piezomagnetic behaviour of WAAM double-shear connection specimens were investigated. It aims to develop a damage monitoring method based on piezomagnetic effect for WAAM steel bolted connections. Eight WAAM steel coupons with two different print layer orientations were fabricated to measure the mechanical properties of WAAM materials. A total of 32 WAAM double-shear bolted connection specimens with two print layer orientations were designed to analyse the structural properties of WAAM members. A magnetic probe was used to monitor the variations of the piezomagnetic field. The evolutions of piezomagnetic field of transversal and longitudinal WAAM coupons were compared and analysed. Notably, it is found that piezomagnetic field manifests anisotropy consistent with the mechanical properties. The characteristics of five failure modes including net section tension, end-splitting, shear-out, bearing, block shear were discussed. The correlation between the magnetic field–displacement curve and the load-displacement curve was systematically examined. The characteristics of magnetic field of specimens failing by bearing were emphatically explored. The results show that bearing failure can be distinguished from other failure modes by monitoring the magnetic field. In addition, the evolution of magnetic field serves as a predictor of the ultimate capacity and failure of double-shear bolted connections.

Enhancing the Range and Reliability of the Spacer Layer Imaging Method

The spacer layer imaging method (SLIM) is widely used to measure the thickness of additive and lubricant films, in lubricant development and evaluation, and for fundamental research into elastohydrodynamic lubrication and tribofilm formation mechanisms. The film thickness measurement, as implemented on several popular tribometers, provides powerful, non-destructive in-situ mapping of film topography with nanometre-scale height sensitivity. However, the results can be highly sensitive to experimental procedure, machine condition, and image analysis, in some cases reporting unphysical film thickness trends. The prevailing image analysis techniques make it challenging to interrogate these errors, often hiding their multivariate nonlinear behaviour from the user by spatial averaging. Herein, several common ‘silent errors’ in the SLIM measurement, including colour matching to incorrect fringe orders, and colour drift due to the optical properties of the system or film itself, are discussed, with examples. A robust suite of novel a priori and a posteriori methods to address these issues, and to improve the accuracy and reliability of the measurement, are also presented, including a novel, computationally inexpensive circle-finding algorithm for automated image processing. In combination, these methods allow reliable mapping of films up to at least 800 nm in thickness, representing a significant milestone for the utility of SLIM applied to elastohydrodynamic contact.Graphical abstract

An in situ exploration of how Fe/N/C oxygen reduction catalysts evolve during synthesis under pyrolytic conditions

In pursuing cheap and effective oxygen reduction catalysts, the Fe/N/C system emerges as a promising candidate. Nevertheless, the structural transformations of starting materials into Fe- and N-doped carbon catalysts remains poorly characterized under pyrolytic conditions. Here, we explore the evolution of Fe species and track the formation of Fe–N4 site development by employing diverse in-situ diagnostic techniques. In-situ heating microscopy reveals the initial formation of FeOx nanoparticles and subsequent internal migration within the carbon matrix, which stops once FeOx is fully reduced. The migration and decomposition of nanoparticles then leads to carbon layer reconstruction. Experimental and theoretical analysis reveals size-dependent behavior of FeOx where nanoparticles below 7 nm readily release Fe atoms to form Fe–N4 while nanoparticles with sizes >10 nm tend to coalesce and impede Fe–N4 site formation. The work visualizes the pyrolysis process of Fe/N/C materials, providing theoretical guidance for the rational design of catalysts.

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

Exploring quantum confinement signature in nitrogen-functionalized graphene quantum dots: Effective mass approximation (EMA) model insights from computational and experimental analyses

A well-established relationship between bandgap energy and Quantum Dots (QDs) dimension, predicted by Effective Mass Approximation (EMA) models, is a cost-effective and reliable method to evaluate the bandgap energy of QDs. The EMA model sheds light on linearity between bandgap energy (Egap) to their inverse square diameter (1/dn), as a signature of the quantum confinement, with the n value reflecting the dimension of the quantum confinement. This study systematically investigated the power parameter "n" dependence in the bandgap energy of Nitrogen-functionalized Graphene Quantum Dots (N-GQDs) with distinct C–N configurations, i.e., Pyridinic-N, Pyrrolic-N, Graphitic-N based on computational and experimental data. The results showed that the calculated bandgap energy values reveal a notable weakening of the size dependence in N-GQDs compared to pristine GQDs, particularly in Pyrrolic-N GQDs, indicative of a unique geometric dimensionality of confinement. This systematic exploration advances our understanding of "n" dependence and bandgap energy in N-GQDs, laying a solid groundwork for modulating the bandgap energy of N-GQDs for optoelectronics and quantum device applications.

CaSDa24: Latest updates to the Dijon calculated spectroscopic databases

We introduce CaSDa24, the latest iteration of our high-resolution molecular spectroscopy database cluster. Comprising ten independent databases, each dedicated to a specific molecule (CH4, CH3Cl, SF6, C2H4, CF4, GeH4, RuO4, SiF4, UF6, SiH4), CaSDa24 integrates updated and new databases since our previous publication. These databases include detailed lists of calculated spectral lines, eigenvectors, energy levels, and additional relevant information derived from the latest experimental spectra analyses. This article provides an overview of the current state of molecular spectroscopy databases, explains the foundational principles of CaSDa24, and summarizes the significant updates and new data introduced.

Asymmetric Cu-N1O3 Sites Coupling Atop-type and Bridge-type Adsorbed *C1 for Electrocatalytic CO2-to-C2 Conversion.

2D functional porous frameworks offer a platform for studying the structure-activity relationships during electrocatalytic CO2 reduction reaction (CO2RR). Yet challenges still exist to breakthrough key limitations on site configuration (typical M-O4 or M-N4 units) and product selectivity (common CO2-to-CO conversion). Herein, a novel 2D metal-organic framework (MOF) with planar asymmetric N/O mixed coordinated Cu-N1O3 unit is constructed, labeled as BIT-119. When applied to CO2RR, BIT-119 could reach a CO2-to-C2 conversion with C2 partial current density ranging from 36.9 to 165.0 mA cm-2 in flow cell. Compared to the typical symmetric Cu-O4 units, asymmetric Cu-N1O3 units lead to the re-distribution of local electron structure, regulating the adsorption strength of several key adsorbates and the following catalytic selectivity. From experimental and theoretical analyses, Cu-N1O3 sites could simultaneously couple the atop-type (on Cu site) and bridge-type (on Cu-N site) adsorption of *C1 species to reach the CO2-to-C2 conversion. This work broadens the feasible C-C coupling mechanism on 2D functional porous frameworks.

Numerical and Experimental Verification of Column Web in Transverse Compression

This paper focuses on the problem of steel members of open double symmetrical cross-sections in transverse compression. First, the problem is introduced with examples of its application in steel structures of buildings and followed by the overview of selected substantial literature resources and current design codes provisions. The essential part of the paper is a presentation of results of experimental tests and advanced numerical analysis of members subjected to selected cases of transverse compression. Where possible, the results are compared with resistances obtained using provisions in currently valid design codes for steel structures. The influence of selected parameters on the resistance in transverse compression is quantified within the evaluation with specific attention paid to plate buckling of the web. Based on data obtained from the experimental and numerical investigations, the most notable findings are summarized.

Bringing back Galium aparine L. from forgotten corners of traditional wound treatment procedures: an antimicrobial, antioxidant, and in-vitro wound healing assay along with HPTLC fingerprinting study

BackgroundThe wound healing process, restoring the functionality of the damaged tissue, can be accelerated by various compounds. The recent experimental analysis highlights the beneficial effects of phytochemicals in improving skin regeneration and wound healing. In traditional medicine, one of the widespread plants used for treating different injuries or skin afflictions is Galium aparine L. (GA). Besides, previously reported chemical compounds of GA suggested its therapeutic effects for the wound healing process, yet its regulatory effects on the cellular and molecular stages of the wound healing process have not been investigated.MethodsIn the present study, the phytochemical profile of the GA extract was analyzed using HPTLC fingerprinting, and further scientific evaluation of its phytochemicals was done. The wound-healing effects of GA extract were explored at the cellular and molecular levels while accounting for cell toxicity. The wound closure enhancing effect, antibacterial activity, and antioxidant activity were assessed.ResultsThe HPTLC fingerprinting of the GA extract proved its previously reported phytochemical profile including phenols, flavonoids, tannins, plant acids, ergot alkaloids, flavonoids, anthraquinones, terpenoids, sterols, salicin, lipophilic compounds, saponins, iridoids, and heterocyclic nitrogen compounds. Antimicrobial assessment, of the extract, indicated the more susceptibility of S. aureus to the inhibitory effects of GA rather than E. coli and S. epidermidis. DPPH test results revealed the antioxidant property of GA extract, which was comparable to ascorbic acid. The results of the viability assay showed no cytotoxicity effects on human umbilical endothelial cell (HUVEC) and normal human dermal fibroblast (NHDF) cell lines treated with different concentrations of whole plant extract and cell viability increased in a dose-dependent manner. The results of the scratch assay showed improved cell migration and wound closure.ConclusionsThis study shows the anti-oxidant, anti-microbial, and in vitro wound healing wound-healing effects of GA hydroalcoholic extract, which aligns with its use in traditional medicine. No cytotoxicity effects were shown. The results from this study can be the basis for further investigations such as animal models and phytochemical studies. Further evaluation of its effects on mechanisms and signaling pathways involved in the wound healing processes such as angiogenesis and cell proliferation can provide novel insights into the potential therapeutic effects of the GA extract.

Exploring View-Specific Label Relationships for Multi-View Multi-Label Feature Selection

In the domain of multi-view multi-label (MVML) learning, features are distributed across various views, each offering multiple semantic representations. While existing approaches aim to balance commonality and complementarity within the view space, the inconsistency in label space has been underexplored, revealing the inadequacy of assuming uniform labels. Thus, there is a pressing need to explore the relationship among view-specific labels. Our method diverges from previous approaches by focusing on imposing constraints tailored to learning view-specific labels. We aim to preserve common characteristics through inter-view relationships while retaining specific traits inherent to each view's instances. By employing these strategies, we establish a clear mapping between labels and feature representations, enabling precise feature weighting. Convergence to the optimal feature set is achieved through multiplicative updating rules. Our method demonstrates superiority across most of cases through comprehensive experimental analysis compared to existing state-of-the art alternatives.

From minimum-viable-products to full models: a step-wise development of diagnostic forward models in support of design, analysis and modelling on the ST40 tokamak

Abstract Like most magnetic confined fusion experiments, the ST40 tokamak started off with a small subset of diagnostics and gradually increased the diagnostic set to include more complex and comprehensive systems. To make the most of each operational phase, forward models of various diagnostics are used and developed to aid design, provide consistency-checks during commissioning, test analysis methods, and build workflows to constrain high-level parameters to inform interpretation, theory and modelling. For new models and new analysis workflows, minimum-viable-products (MVP) are released early, and their complexity is increased in a step-wise manner, facilitating the support of all programme phases on multiple parallel applications, while enabling learning opportunities and feedback loops. In this contribution we review the philosophy, scope and architecture of the framework under development. We discuss the details of some forward models, with examples on how they are used to aid diagnostic design, to investigate analysis methodologies through synthetic data, and how they are embedded in experimental analysis workflows. We compare previously published experimental results with new, more advanced analysis workflows employing more recent, detailed models and new diagnostic data, providing confirmation of the published material from the 2021-22 experimental campaign.

Modified affine projection sign algorithm for power quality enhancement and metaheuristic-based MPPT in grid-interactive solar PV system: an experimental analysis

Modified affine projection sign algorithm for power quality enhancement and metaheuristic-based MPPT in grid-interactive solar PV system: an experimental analysis

Investigation of Convective Heat Transfer and Stability on a Rotating Disk: A Novel Experimental Method and Thermal Modeling

Experimental and numerical investigations are conducted on a rotating disk from the perspective of convective heat transfer to understand the effect of heating on the stability of flow. A non-invasive approach with a thermal camera is employed to determine local Nusselt numbers for different rotational rates and perturbation parameters, i.e., the strength of the heat transfer. A novel transient temperature data extraction over the disk radius and an evaluation method are developed and applied for the first time for the air on a rotating disk. The evaluation method utilizes the lumped capacitance approach with a constant heat flux input. Nusselt number distributions from this experimental study show that there is a good agreement with the previous experimental correlations and linear stability analysis on the subject. A significant result of this approach is that by using the experimental setup and developed approach, it is possible to qualitatively show that instability in the flow starts earlier, i.e., an earlier departure from laminar behavior is observed at lower rotational Reynolds numbers with an increasing perturbation parameter, which is due to the strength of heating. Two experimental setups are modeled and simulated using a validated in-house Python code, featuring a three-dimensional thermal model of the disk. The thermal code was developed for the rotating disks and brake disks with a simplified geometry. Experimentally evaluated heat transfer coefficients are implemented and used as convective boundary conditions in the thermal code. Radial temperature distributions are compared with the experimental data, and there is good agreement between the experiment and the model. The model was used to evaluate the effect of radial conduction, which is neglected when using the lumped capacitance approach to determine heat transfer coefficients. It was observed that the radial conduction has a slight effect. The methodology and approach used in this experimental study, combined with the numerical model, can be used for further investigations on the subject.

Experimental analysis of enhanced finite set model predictive control and direct torque control in SRM drives for torque ripple reduction

The magnet-less switched reluctance motor (SRM) speed-torque characteristics are ideally suited for traction motor drive characteristics and its advantage to minimize the overall cost of on-road EVs. The main drawbacks are torque and flux ripple, which have produced high in low-speed operation. However, the emerging direct torque control (DTC) operated magnitude flux and torque estimation with voltage vectors (VVs) gives high torque ripples due to the selection of effective switching states and sector partition accuracy. On the other hand, the existing model predictive control (MPC) with multiple objective and optimization weighting factors produces high torque ripples due to the system dynamics and constraints. Therefore, existing DTC and MPC can result in high torque ripples. This paper proposed a finite set (FS)-MPC with a single cost function objective without weighting factor: the predicted torque considered to evaluate VVs to minimize the ripples further. The selected optimal VV minimizes the SRM drive torque and flux ripples in steady and dynamic state behaviour. The classical DTC and proposed model were developed, and simulation results were verified using MATLAB/Simulink. The proposed model operated in SRM drives experimental results to prove the effective minimization of torque and flux ripples compared to the existing DTC.

Numerical and Experimental Modal Analysis of a Gyroid Inconel 718 Structure for Stiffness Specification in the Design of Load-Bearing Components

The study aims to investigate the modal properties of a 60 × 70 × 80 mm gyroid structure made of Inconel 718 with 67.5% porosity. The geometry model for sample production was created using the software PTC Creo, whereas the geometry model for numerical analysis was created using the Python application ScaffoldStructures. FE analysis was performed using ANSYS 2024 R1 software. Free boundary conditions were used in experimental modal analysis to ensure feasibility. The analysis identified the first four natural frequencies ranging from 10 to 16 kHz. The results revealed that the first natural frequency corresponds to the first torsional frequency about the Z axis, the second to the first flexural mode in the XZ plane, the third to the first bending mode in the YZ plane, and the fourth to the first torsional mode about the X axis. Small differences between the results of numerical and experimental modal analysis can be attributed to geometric errors in the manufactured sample, careless removal from the platform, and due to reduction in the complexity of the numerical FE model. Employing modal analysis of a component, the stiffness of a lightweight component can be revealed. In the case of the sample with the cellular structure of gyroid type, relatively high stiffness regarding the material savings was identified, which can be advantageously used in many applications.

Accurate identification of low-resistivity gas layer in tight sandstone gas reservoirs based on optimizable neural networks

In tight sandstone reservoirs, low resistivity gas reservoirs have a certain gas production capacity, but their reservoir resistivity value is less than 20 Ω m, or their ratio to water layer resistivity is less than 2. Therefore, the identification of low resistivity gas reservoirs is a prominent problem that needs to be resolved. In this paper, on the basis of traditional experimental analysis, NMR experiments and wettability experiments were conducted. The high bound water saturation and pore fractal dimension have a significant impact on reservoir resistivity. After various experimental analysis results were preprocessed by the image pool and data pool, an optimized neural network (ONN) classification model was established based on deep learning theory. The confusion matrix result (training datasets) was 87.13%, and the ROC area of low resistivity gas layers was close to 1, indicating good recognition performance for low resistivity gas layers. The model was used to identify the properties of the J4 well reservoir section. The identification result was consistent with the actual logging resistivity values (low resistivity) and testing conclusions (gas layer). This model can accurately identify low resistivity gas reservoirs in the research area, and has important guiding significance for identifying the reservoir properties of tight sandstone gas reservoirs in the Sichuan Basin. It can provide reference value for the exploration and development of similar gas reservoirs in other regions.

Experimental Analysis on Using Nanoparticles to Improve Heat Transfer in Compact Heat Exchangers

Compact heat exchangers are now essential parts of many industrial processes that aim to reduce their environmental impact and increase energy efficiency. The experimental inquiry presented in this paper aims to improve the performance of heat transfer in compact heat exchangers by adding nanoparticles to the fluid used for heat transfer.A range of experiments were carried out with different nanoparticle concentrations, flow rates, and temperatures to evaluate the effects of nanofluids.To explore the effects on heat transfer enhancement, a variety of nanoparticle materials, including graphene oxide, copper oxide, titanium dioxide, zinc oxide, and aluminum oxide, were dispersed in the base fluids. According to experimental data, adding nanofluids significantly improved heat transfer performance; these nanofluids demonstrated greater heat transfer coefficients than conventional heat transfer fluids.

Impact of ternary nanocomposite In2O3/SnO2/F-CDs composition on low ppm NO2 sensing performance

Nitrogen dioxide (NO2) is a significant air pollutant with a notable environmental impact, necessitating precise monitoring. However, most existing NO2 sensing materials exhibit low sensitivity and require high-temperature operation. In this study, we employed a hydrothermal and co-precipitation method to synthesize a novel ternary nanocomposite, In2O3/SnO2 nanoparticles (NPs)/fluorine-doped carbon quantum dots (F-CDs). By varying the mass ratio of In2O3 to SnO2 NPs, we investigated the gas-sensing performance of the composite towards NO2. The results demonstrate that when the ratio of SnO2 and In2O3 reaches to 1 : 2, the 2-In2O3/SnO2/F-CDs gas sensor exhibited the highest response, excellent selectivity, and remarkable stability under ultraviolet (UV) light irradiation at room temperature (∼25 °C). Furthermore, the response of the 2-In2O3/SnO2/F-CDs gas sensor displayed a strong linear relationship with the concentration of NO2 gas. At a NO2 gas concentration of 3 ppm, the sensitivity was 238, which is significantly higher than that of In2O3/SnO2 NPs and In2O3 sensors. The response time and recovery time were measured at 62 s and 33 s, respectively. Finally, based on experimental characterization results and band structure analysis, we confirmed the promising effects of ternary nanocomposite in enhancing gas sensing.

Experimental And Numerical Analysis of Impact Strength of Fiber Metal Laminates Made of Aluminium with GFRP

Experimental And Numerical Analysis of Impact Strength of Fiber Metal Laminates Made of Aluminium with GFRP

Numerical and Experimental Analysis of Mechanical Properties of Glass Fibre with Pet Bottle Yarns Layer

Numerical and Experimental Analysis of Mechanical Properties of Glass Fibre with Pet Bottle Yarns Layer