黑料网

Additive manufacturing; Direct energy deposition; Soft magnetic material; Silicon steel

Introduction

Since these physical effects result from the collective behaviour of relativistic interactions between electrons, which are influenced by anisotropy and inhomogeneity, developing a fundamental mathematical model to fully describe ferromagnetic behaviour in soft magnetic materials (SMMs) presents an apparently insurmountable challenge [2]. In his analysis of SMMs, Fish notes that although “it is not yet practicable to compute from basic principles” their B-H curves and accompanying power losses, “SMMs are key constituents of practically every electrical gadget of modern civilization.” B-H curves must therefore be established empirically.

This paper’s primary goal was to discuss the power loss PV (per unit volume) resulting from an SMM with a rate-independent hysteresis curve in the low applied field limit. In technical applications where device function repeatability and energy efficiency are crucial, these two restrictions are significant. In applications requiring SMMs with unsaturated magnetizations and low energy losses, such as inductive power transmission for electrical car charging and electrical machines, these two restrictions, for instance, can be crucial [3]. In reality, the power loss of soft magnetic composite materials based on polymers will frequently fall under that heading. Since these materials have an inhomogeneous structure with magnetic particles separated by a diamagnetic matrix, they frequently show lower permeability, high saturation, and low losses. We have also covered power loss for huge saturating fields, though, to be thorough [4]. The magnitude and direction of the periodic inducing field H, the history, composition, structure, shape, stress state, and temperature of the SMM, the frequency of the periodic electrical current source and the nature of its periodic wave form, as well as the measuring system, all affect the volumetric power loss from an SMM exposed to the field. total loss of volumetric power Commonly, Ptotal is classified into three mechanisms: P total=PV+ Pclass+ Panom where PV is the rate-independent volumetric power loss resulting from the domain processes that this study is interested in , Pclass is the classical eddy current loss, and Panom is the anomalous or excess volumetric power loss that is not taken into account by PV+Pclass. This research mainly examines situations where eddy current and abnormal power loss are insignificant in comparison to PV, resulting in PtotalPV. The requirement that the energy loss in one hysteresis loop be rate independent will be a design requirement in many situations. Despite these restrictions, a variety of power loss behaviours are still permitted due to the variable hysteresis response [5-8].

While acknowledging that B-H curves must now be found empirically, this study investigates if there are classes of SMMs of industrial significance for which an approximate theory for PV from minor loops can be built using the characteristics of the associated main loop. Research programmes producing numerous different samples of SMMs might find such extrapolation appealing because the minor loop was of interest [9].

Materials and Methods

Pretreatment of regolith

A maximum diameter of 63 m was applied to the sieving process for all regolith simulant samples. For subsequent trials, only particles smaller than 63 m were utilised.

Creating a specified minimum medium

A mixture of 100 mM NaCl, 50 mM sodium 4-(2-hydroxyethyl)- 1-piperazineethanesulphonic acid (HEPES), 7.5 mM NaOH, 16 mM NH4Cl, 1.3 mM KCl, 4.3 mM NaH2PO4•2H2O, and 10 mL/L trace mineral supplement (ATCC® MD-TMSTM) was used to create the required minimum medium The prescribed minimal medium was autoclaved, then 10 mL/L of vitamin solution (ATCC® MD-VSTM), 20 mg/L of L-arginine hydrochloride, 20 mg/L of L-glutamate, 20 mg/L of L-serine, and 20 mM lactate were added.

Sample preparation

EAC-1, JSC-2A, and JSC-Mars1 regolith simulant samples were autoclaved at 121°C without the use of any extra chemicals to sterilise them. The samples were diluted in TSB or another predetermined minimum medium to the required concentration. The 15 mL Falcon tubes were used to prepare the aerobic samples. The anaerobic samples were made in anaerobic culture tubes from Sigma Aldrich, and the oxygen content of the solutions was reduced by three flushings with 100% nitrogen gas. To a final O.D.600 of 0.05, overnight cultures of Shewanella oneidensis were applied to the samples. Aluminum lids and sterile, non-toxic chlorobutyl stoppers were used to seal the anaerobic samples (Sigma Aldrich).

Toxicity of regolith simulants

Aerobic and anaerobic samples containing regolith diluted in TSB at various concentrations (0 g/L, 0.1 g/L, 1 g/L, 10 g/L, and 100 g/L) were used for the toxicity investigation. After incubation at 30°C with constant shaking (250 rpm) under aerobic or anaerobic conditions, the bacterial growth in the samples was measured using optical density (O.D.) measurements in a plate reader (Tecan endless M200 pro microplate reader) at a wavelength of 600 nm. For 48 hours, absorbance was measured every 5 minutes. The samples were evaluated three times.

The same samples were utilised to measure colony forming units concurrently (CFU). Every sample was prepared following a logarithmic dilution curve at a 107 dilution before being plated out in triplicate onto LB agar plates. Phosphate-buffered saline (PBS) solution was used for dilution. At room temperature (20–22°C), samples were cultured for 24 hours before individual colonies were counted.

Small-scale magnetic extraction

A neodymium magnet and three 3 mL cuvettes from Sigma Aldrich made comprised the magnetic extraction setup (60x10x3 mm, coated with Ni-Cu-Ni, MIKEDE). Two layers of aluminium foil and two layers of Parafilm were tightly coiled around the magnet’s lower half. The design made it simple to take out and put back in the magnet. One millilitre of the sample was placed in the first cuvette, while MilliQ water was placed in the second and third [10]. The first cuvette was twice filled with the magnet with lid. The magnet’s cover attracted magnetic material, which was then transported to the second cuvette together with the magnet and related magnetic material. To enable the magnetic substance to sink into the cuvette, the magnet was carefully removed from the cover. Once more inserting the magnet to draw only magnetic material, it was then used to transfer that material into a third pre-weighted cuvette. An second neodymium magnet was positioned beneath the cuvette in order to suspend the magnetic substance within the MilliQ water. After the cuvettes had dried, the liquid that the magnet had not drawn to it was decanted, and they were weighted. After adding the extracted material, the weight of each individual cuvette was deducted from the overall weight [11].

Fe(II)_(aq) concentration determination

The colorimetric approach, a variation of the phenanthroline method, was used to detect the amounts of aqueous ferrous iron. A complexing reagent was created by combining 0.6 grammes of sodium fluoride (Sigma-Aldrich, 99%) with 28 mL of MilliQ water and 0.57 mL of sulfuric acid (99.999%). 0.2 grammes of 1,10-Phenanthroline monohydrate (Sigma-Aldrich, 99%) were dissolved in 30 L of hydrochloric acid (37%) and 7 mL of MilliQ water to create an o-Phenanthroline solution. 10 mL of MilliQ water was added after the complexing agent had been mixed.

Large-scale magnetic extraction

The preparation of a 10 g/L solution of previously sieved (63 m) EAC-1, JSC-2A, or JSC-Mars1 regolith simulants with TSB medium allowed for large-scale magnetic extractions. An overnight culture of Shewanella was added to an O.D.600 of 0.05, and the mixture was shaken at 120 rpm while being cultured for 168 hours at 30°C. Neodymium magnets (60x10x3 mm, coated with Ni-Cu-Ni, MIKEDE) were first wrapped in a layer of Parafilm, which was then followed by two layers of aluminium foil covering the ends of the magnets [12, 13]. The magnet was re-extracted using rubber tweezers and additional neodymium magnets after a 168-hour incubation period during which it was submerged in the solution, swirled five times, and allowed to settle for 30 seconds. The removed magnet was washed in MilliQ water using a 50 mL Falcon tube. The Parafilm magnet-cover was cut apart in the middle, the magnet was taken out, and the cover was washed into a Petri dish that was not already empty. Repeating this technique led to an ineffectively larger yield because there was so little material being removed from each magnet. The leftover material in the flask was rinsed into a different Petri dish to capture the non-magnetic material [14].

Results

In ML algorithms, feature selection is a crucial stage. ML algorithms can perform better with a decent sample dataset. The first set of elements for feature correlation analysis were Dosef, Hform, Natoms, Mass, Cellarea, Energy, Smax, Fmax, and Volume. The Pearson linear correlation coefficient map may examine and detect highly correlated data as well as find and remove various collinearities between features, which can skew or imprecisely estimate a model. This is especially true for linear models like SVR.

Discussion

This study used MRE to examine the apparent change in a soft tissue phantom’s shear modulus around a pressurised inclusion. The effect of a large-strain deformation on the wave equation for a viscoelastic body was taken into account analytically, emphasising how the stiffness tensor is dependent on both the underlying strain and the stress-strain relationship [15]. The idealised instance of an expanding nonlinearly elastic thick-shelled sphere was utilised to simulate the pressure that an expanding tumour would exert on its surroundings in order to derive a mathematical model. However, actual tumours can have considerable eccentricity and/or invasive protrusions that can facilitate invasion and migration. Although some studies have attempted to represent a solid tumour using more complex geometries, most investigations start with an axisymmetric geometry because of the more straightforward analytical formulation [16]. In order to keep the linked tumour mechanics and MRE analytically tractable in this case and to produce benchmark results for secondary geometric effects, we choose to apply the simplifying assumption.

Conclusion

Although the model was built on the assumption that the balloon would inflate asymmetrically, the balloon catheter actually used did not exhibit a spherical shape until 0.4 mL of water were injected. Nevertheless, at all inflation states, the two main axes orthogonal to the catheter’s shaft displayed values that were comparable, indicating a symmetric expansion in the plane transverse to the catheter. In order to observe the effects of this circular stretch on G′ probed by shear wave travelling in the same plane, the transducer’s position and the imaging plane were selected. The mechanical impacts were also sampled from carefully chosen places where confounding influences were minimum, such as those where the signal-to-noise (or effect-to-noise) was anticipated to be maximal, while avoiding the resolution limit at the polar region that progressively hampered the analysis. Analogous patterns were found between the modelled and reconstructed G′ distribution, indicating that various geometrical conditions can give equivalent outcomes even though the experimental setup did not completely replicate the parameters used for the definition of the model. Another analytical effort found comparable pressurestrain curves using various simulated tumor forms, confirming our simplifications and minimizing the practical constraints encountered in creating adequate experimental settings.

Acknowledgement

None

Conflict of Interest

None

1. Jain RK, Martin JD, Stylianopoulos T (2014) . Annu Rev Biomed Eng 16:321-346.

, ,

2. Stylianopoulos T, Martin JD, Chauhan VP, Jain SR, Diop-Frimpong B, et al. (2012) . Proc Natl Acad Sci 109: 15101-15108.

, ,

3. Voutouri C, Polydorou C, Papageorgis P, Gkretsi V, Stylianopoulos T (2016) . Neoplasia 18:732-741.

, ,

4. Voutouri C, Mpekris F, Papageorgis P, Odysseos AD, Stylianopoulos T (2014) . PLoS One 9:e104717.

, ,

5. Heldin CH, Rubin K, Pietras K, Ostman A (2004) . Nat Rev Cancer 4:806-813.

, ,

6. Stylianopoulos T, Martin JD, Snuderl M, Mpekris F, Jain SR, et al. (2013) . Cancer Res 73: 3833-3841.

, ,

7. Jain RK, Baxter LT (1988) . Cancer Res 48: 7022-7032.

,

8. Baxter LT, Jain RK (1989) . Microvasc Res 37: 77-104.

, ,

9. Venkatesh SK, Yin M, Ehman RL (2013) . J Magn Reson Imaging 37:544-555.

, ,

10. Murphy MC, Huston J, Jack CR, Glaser KJ, Senjem ML, et al. (2013) . PLoS One 8.

, ,

11. Sack I, Beierbach B, Hamhaber U, Klatt D, Braun J (2008) . NMR Biomed 21: 265-271.

, ,

12. Sinkus R, Lorenzen J, Schrader D, Lorenzen M, Dargatz M, et al. (2000) . Phys Med Biol 45:1649-1664.

, ,

13. Kolipaka A, McGee KP, Araoz PA, Glaser KJ, Manduca A, et al. (2009) . Magn Reson Med 62:135-140.

, ,

14. Sack I, Rump J, Elgeti T, Samani A, Braun J (2009) . Magn Reson Med 61:668-677.

, ,

15. Mariappan YK, Glaser KJ, Hubmayr RD, Manduca A, Ehman RL, et al. (2011) . J Magn Reson Imaging 33: 1351-1361.

, ,

16. Talwalkar JA, Yin M, Venkatesh S, Rossman PJ, Grimm RC, et al. (2009) . Am J Roentgenol 193:122-127.

, ,