Oriented Magnetite Inclusions in Plagioclase: Implications for the Anisotropy of Magnetic Remanence

2021 ◽  
Author(s):  
Olga Ageeva ◽  
Gerlinde Habler ◽  
Stuart A. Gilder ◽  
Roman Schuster ◽  
Alexey Pertsev ◽  
...  
Keyword(s):  
Magnetic Remanence

scholarly journals Novel Polyimide/Copper-Nickel Ferrite Composites with Tunable Magnetic and Dielectric Properties

Polymers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1646
Author(s):  
Corneliu Hamciuc ◽  
Mihai Asandulesa ◽  
Elena Hamciuc ◽  
Tiberiu Roman ◽  
Marius Andrei Olariu ◽  
...  
Keyword(s):  
Dielectric Properties ◽  
Nickel Ferrite ◽  
Scanning Calorimetry ◽  
X Ray ◽  
Broadband Dielectric Spectroscopy ◽  
Copper Nickel ◽  
Low Dielectric ◽  
Magnetic Remanence ◽  
Polyimide Matrix ◽  
Ferrite Composites

Heat-resistant magnetic polymer composites were prepared by incorporating cerium-doped copper-nickel ferrite particles, having the general formula Ni1-xCuxFe1.92Ce0.08O4 (x: 0.0, 0.3, 0.6, 1.0), into a polyimide matrix. The effects of particle type and concentration on the thermal, magnetic, and electrical properties of the resulting composites were investigated. The samples were characterized by FTIR, scanning electron microscopy, X-ray diffractometry, thermogravimetric analysis, differential scanning calorimetry, vibrating sample magnetometer, and broadband dielectric spectroscopy. The composites exhibited high thermal stability, having initial decomposition temperatures between 495 and 509 °C. Saturation magnetization (Ms), magnetic remanence (Mr), and coercivity (Hc) were found in range of 2.37–10.90 emu g−1, 0.45–2.84 emu g−1, and 32–244 Oe, respectively. The study of dielectric properties revealed dielectric constant values of 3.0–4.3 and low dielectric losses of 0.016–0.197 at room temperature and a frequency of 1 Hz.

Classifying Copper-Molybdenum-Gold Porphyry Deposit Alteration using Magnetic and Spectral data

2021 ◽  
Author(s):  
Oliver Dixon ◽  
William McCarthy ◽  
Nasser Madani ◽  
Michael Petronis ◽  
Steve McRobbie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Susceptibility ◽  
Spectral Data ◽  
Learning Algorithm ◽  
Copper Production ◽  
Magnetic Data ◽  
Machine Learning Algorithm ◽  
Porphyry Deposit ◽  
Remanent Magnetisation ◽  
Magnetic Remanence

<p>Copper is one of the most important critical metal resources needed to achieve carbon neutrality with a projected increase in demand of >300% over the next half century from electronics and renewables.  Porphyry deposits account for most of the global copper production, but the discovery of new reserves is ever more challenging. Machine learning presents an opportunity to cross reference new and traditionally under-utilised data sets with a view to developing quantitative predictive models of hydrothermal alteration zones to guide new, ambitious exploration programs.</p><p>The aim of this study is to demonstrate a new alteration classification scheme driven by quantitative magnetic and spectral data to feed a machine learning algorithm. The benefits of an alteration model based on quantitative data rather than subjective observations by geologists, are that there is no bias in the data collected, the arising model is quantifiable and therefore easy to model and the process be fully automated. Ultimately, this approach aids more detailed exploration and mine modelling, in turn, reducing the extraction process carbon footprint and more effectively identifying new deposits.</p><p>Presented here are magnetic susceptibility and shortwave infrared (SWIR) data collected from the KazMinerals plc. owned Aktogay Cu-Mo giant porphyry deposit, eastern Kazakhstan, which has a throughput of 30Mtpa of ore. These data are cross referenced using a newly developed machine learning algorithm. Generated autonomously, our results reveal twelve statistically and geologically significant clusters that define a new alteration classification for porphyry style mineralisation. Results are entirely non-subjective, reproducible, quantitative and modellable.</p><p>Importantly, magnetic susceptibility measurements improve the algorithm’s ability to identify clusters by between 29-36%; enhancing the sophistication of the included magnetic data promises to yield substantially better statistical results. Magnetic remanence data are therefore being complied on representative samples from each of the twelve identified clusters, including hysteresis, isothermal remanent magnetisation (IRM) acquisition, FORC measurements, natural remanent magnetisation (NRM) and anhysteretic remanent magnetisation (ARM). Through collaboration with industry partners, we aim to develop an automated means of collecting these magnetic remanence data to accompany the machine learning algorithm.</p>

scholarly journals Palaeomagnetic results from the Lopra-1/1A re-entry well, Faroe Islands

2006 ◽  
Vol 9 ◽  
pp. 51-65 ◽  
Author(s):  
Niels Abrahamsen
Keyword(s):  
Magnetic Susceptibility ◽  
Faroe Islands ◽  
Solid Core ◽  
Magnetic Remanence ◽  
Normal Polarity ◽  
Geophysical Logs ◽  
Exposed Part ◽  
Geomagnetic Polarity ◽  
Geomagnetic Polarity Time Scale ◽  
Made In

The palaeomagnetic dating and evolution of the Faroe Islands are discussed in the context of new density and rock magnetic results from the deepened Lopra-1/1A well. The reversal chronology of the c. 6½ km thick basalt succession is also described. The polarity record of the Faroe Islands may now be correlated in detail with the Geomagnetic Polarity Time Scale. The lowermost (hidden) part of the lower basalt formation correlates with Chron C26r (Selandian age), the top (exposed) part of the lower basalt formation correlates with Chrons C26n, C25r and C25n (Selandian and Thanetian age) and the middle and upper basalt formations correlate with Chron C24n.3r (Ypresian). Inclinations indicate a far-sided position of the palaeomagnetic poles, which is characteristic of results from most Palaeogene volcanics from the northern North Atlantic region. The density, magnetic susceptibility and magnetic remanence of 20 specimens from one solid core (1½ m in length) and 26 sidewall cores from the well between –2219 and –3531 m below sea level (b.s.l.) suggest that the volcanic materials can be divided into two characteristic groups: solid unaltered basalts and altered basalts and tuffs. The magnetic properties are typically log-normally distributed and the carriers of remanence are Ti-poor Ti-magnetites with Curie temperatures close to 580°C. The inclination of the 1½ m core at 2380 m b.s.l. is dominantly negative (two plugs at the very top of the core do show normal polarity, but they are likely to be misoriented as all specimens appear to be from one flow). Magnetic logging (magnetic susceptibility and field intensity) down to 3515 m b.s.l. was made in Lopra-1/1A together with other geophysical logs but did not yield conclusive inclination data.

Synthesis and Magnetic Properties of SrFe12-XCoxO19 (x= 0- 2) Hexaferrite Nanoparticles

2012 ◽  
Vol 622-623 ◽  
pp. 925-929 ◽  
Author(s):  
M. Zargar Shoushtari ◽  
S E. Mousavi Ghahfarokhi ◽  
F. Ranjbar
Keyword(s):  
Magnetic Properties ◽  
Magnetic Measurements ◽  
Sol Gel ◽  
Anisotropy Field ◽  
Strontium Hexaferrite ◽  
Nominal Composition ◽  
Second Phase ◽  
Energy Product ◽  
Crystalline Anisotropy ◽  
Magnetic Remanence

In this paper, a batch of M- type strontium hexaferrite samples with nominal composition of SrFe12-xCoxO19(where x= 0- 2), have been synthesized via sol- gel method. In the synthesis of samples, first a precursor gel was prepared, and then dry- gel was calcined at 1000°C for 2 hours to obtain the nano- SrFe12-xCoxO19. The XRD results revealed that for SrFe12-xCoxO19 samples with x≤0.5, all of them have single- phase hexaferrite structure and also this data suggests that the F 3+ ions are substituted by Co2+ ions in the crystallography sites of the SrFe12-xCoxO19, but for the samples with x>0.5, the second phase of CoFe2O4 is appeared and suggests that the Co2+ ions also make a distinct phase in the samples. The magnetic properties, such as saturation magnetization (Ms), magnetic remanence (Mr), magnetic coercivity (Hc), squareness ratio (Mr/Ms), crystalline anisotropy field (Ha), energy product [(BH)max] and the susceptibility χ as the derivative of M with respect to H of the upper branch of the hysteresis loop were discussed by measurements of M-H curves with vibrating sample magnetometer (VSM). The magnetic measurements revealed that the coercively (Hc) values of all the samples decrease with increasing dopant contents.

scholarly journals White Magnetic Paper with Zero Remanence Based on Electrospun Cellulose Microfibers Doped with Iron Oxide Nanoparticles

Nanomaterials ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 517 ◽  
Author(s):  
G. Papaparaskeva ◽  
M. M. Dinev ◽  
T. Krasia-Christoforou ◽  
R. Turcu ◽  
S. A. Porav ◽  
...  
Keyword(s):  
Iron Oxide ◽  
Cellulose Acetate ◽  
Iron Oxide Nanoparticles ◽  
Volume Fraction ◽  
White Paper ◽  
Oxide Nanoparticles ◽  
Magnetic Iron Oxide Nanoparticles ◽  
Magnetic Remanence ◽  
Cellulose Microfibers ◽  
Fibrous Membranes

The preparation procedure of zero magnetic remanence superparamagnetic white paper by means of three-layer membrane configuration (sandwiched structure) is presented. The cellulose acetate fibrous membranes were prepared by electrospinning. The middle membrane layer was magnetically loaded by impregnation with an aqueous ferrofluid of 8 nm magnetic iron oxide nanoparticles colloidally stabilized with a double layer of oleic acid. The nanoparticles show zero magnetic remanence due to their very small diameters and their soft magnetic properties. Changing the ferrofluid magnetic nanoparticle volume fraction, white papers with zero magnetic remanence and tunable saturation magnetization in the range of 0.5–3.5 emu/g were prepared. The dark coloring of the paper owing to the presence of the black magnetite nanoparticles was concealed by the external layers of pristine white cellulose acetate electrospun fibrous membranes.

Magnetic Remanence Prediction of NdFeB Magnets Based on a Novel Machine Learning Intelligence Approach Using a Particle Swarm Optimization Support Vector Regression

2014 ◽  
Vol 6 (4) ◽  
pp. 72-81 ◽  
Author(s):  
WenDe Cheng
Keyword(s):  
Magnetic Properties ◽  
Particle Swarm Optimization ◽  
Support Vector Regression ◽  
Particle Swarm ◽  
Support Vector ◽  
Chemical Compositions ◽  
Swarm Optimization ◽  
Ndfeb Magnets ◽  
Magnetic Remanence ◽  
The Mean

Studies have shown that the chemical compositions affecting the magnetic properties of NdFeB magnets. In order to get the right NdFeB magnets, it is advantageous to have an accurate model with which one can predict the magnetic properties with different components. In this paper, according to an experimental dataset on the magnetic remanence of NdFeB, a predicting and optimizing model using support vector regression (SVR) combined with particle swarm optimization (PSO) was developed. The estimated result of SVR agreed with the experimental data well. Test results of leave-one-out cross validation show that the mean absolute error does not exceed 0.0036, the mean absolute percentage error is solely 0.53%, and the correlation coefficient () is as high as 0.839. This implies that one can estimate an available combination of different proportion components by using support vector regression model to get suitable magnetic remanence of NdFeB.

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