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Alloys

Alloys are metallic materials composed of two or more chemical elements, at least one of which is a metal.
These materials are engineered to exhibit enhanced properties compared to their individual components, such as increased strength, corrosion resistance, or thermal conductivity.
Alloys are widely used in a variety of industries, including aerospace, automotive, construction, and electronics.
Researchers continually explore new alloy compositions and production methods to optimize performance and cost-effectiveness.
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Most cited protocols related to «Alloys»

The BIS/BAS Scales (Carver & White, 1994 ) and SPSRQ (Torrubia et al., 2001 ) were used to select the risk groups. The BIS/BAS is the most frequently used self-report measure to assess individual differences in BIS and BAS sensitivity. It consists of 20 items on 4-point Likert scales (1 = strongly disagree, 4 = strongly agree) and is comprised of three BAS subscales and one BIS subscale. We used the BAS-Total (BAS-T) score, calculated as the sum of all BAS items, as one of our screening measures. The BIS/BAS has demonstrated internal consistency and retest reliability (Carver & White, 1994 ), as well as construct validity, exhibiting expected associations with prefrontal cortical activity, affect, personality traits, and performance on reaction-time and learning tasks involving incentives (Colder & O'Conner, 2004 (link); Harmon-Jones & Allen, 1997 (link); Kambouroupolis & Staiger, 2004; Sutton & Davidson, 1997 ; Zinbarg & Mohlman, 1998 (link)). Internal consistencies of the BAS-T and BIS scales in the Phase I screening sample were α’s = .80 and .72, respectively.
The SPSRQ was designed to improve on the BIS/BAS by including items that focus on sensitivity to specific types of rewards and punishments, whereas the BIS/BAS items focus on generalized sensitivity to punishment and reward. The SPSRQ has 24 SR (e.g., “Does the good prospect of obtaining money motivate you strongly to do some things?”; “Do you often do things to be praised?”) and 24 SP (“Do you often refrain from doing something because you are afraid of it being illegal?”; “Is it difficult for you to telephone someone you do not know?”) “yes” or “no” items designed to assess BAS and BIS sensitivity, respectively. Both subscales have acceptable internal consistency, with α’s = .75–.83 (Torrubia et al., 2001 ). In Phase I of this study, α’s for the SR and SP scales were .76 and .84, respectively. Three-month retest reliabilities are .87 for the SR and .89 for the SP scale (Torrubia et al., 2001 ). Findings also support the construct validity of the SPSRQ in terms of expected correlations with extraversion, impulsivity, sensation seeking, and neuroticism, and associations with proneness to various personality disorders (e.g., Alloy et al., 2006b (link); Caseras, Torrubia & Farre, 2001; Torrubia et al., 2001 ). BAS-T and SR scores correlated r = .40 in our Phase I sample.
Publication 2011
Alloys Cold Temperature Extraversion, Psychological Fear Hypersensitivity Neuroticism Personality Disorders Population at Risk Prefrontal Cortex
The Adolescent Cognitive Style Questionnaire-Modified (ACSQ-M; Hankin & Abramson, 2002 (link)) is a modified version of the original ACSQ that assesses inferential style regarding the causes, consequences, and self-worth implications of negative life events, the cognitive vulnerability to depression featured in HT. The original ACSQ assessed inferential style for negative events in the achievement and interpersonal domains, whereas the ACSQ-M also assesses inferential style in the appearance domain, another content area of importance to adolescents. Adolescents are presented with 12 hypothetical achievement, interpersonal, or appearance negative events (4 per domain) and are asked to make inferences about the causes (internal-external, stable-unstable, and global-specific), consequences, and self-worth implications of each hypothetical event. Each dimension is rated from 1 to 7, with higher scores indicating a more negative cognitive style. Several scores from the ACSQ-M were examined in the present study. First, to be comparable with previous studies using the original ACSQ, each adolescent’s overall negative composite score was calculated based on a sum of the stability, globality, consequences, and self dimensions across the achievement and interpersonal domains, following Alloy et al. (2000 (link); 2006 (link)). The negative composite score for each content domain (achievement, interpersonal, appearance) was also calculated. Second, following Abela and Sarin (2002) , each adolescent’s weakest link score was derived as his/her most negative inferential style dimension. Finally, because negative inferential style may not have fully consolidated by ages 12–13 and prior research suggests that specificity of inferential style to depression vs. other psychopathology may vary by dimension, we also examined scores for each individual inferential style dimension (internality, stability, globality, consequences, self), calculated as the sum of items tapping that dimension across the achievement and interpersonal domains. The ACSQ has demonstrated excellent internal consistency, good retest reliability, and adequate factor structure as a measure of HT’s cognitive vulnerability to depression among adolescents (Hankin & Abramson, 2002 (link)). Internal consistencies in this sample were α = .94 for the overall negative composite, and .78 – .87 for the 5 individual dimensions.
Publication 2012
Adolescent Alloys Cognition Debility Eye factor A Sarin

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Publication 2011
Adolescent Aerosols Alloys Autoimmune Lymphoproliferative Syndrome Child Metals Parkinsonian Disorders Plants Polytetrafluoroethylene Student Teflon Voluntary Workers
To develop the carved depression and mania scales, secondary analyses pooled nine samples: two clinical youth samples and seven non-clinical adult samples (Supplemental Table 1). Of 741 youths, 83 met strict DSM-IV criteria for BD I, and 118 for other bipolar spectrum diagnoses; 266 for unipolar depression, 204 for ADHD or disruptive behavior disorders without comorbid mood disorder, and 67 with a variety of other diagnoses. The median number of Axis I diagnoses per youth was 3.0. The adult samples were largely students, and Supplemental Table 1 shows that adult modal age was early-mid 20s.
General Behavior Inventory (GBI, Depue et al., 1981 (link)). The GBI identifies lifetime diagnoses of BD as well as syndromal and subsyndromal affective tendencies in clinical and non-clinical populations (Danielson et al., 2003 (link); Depue et al., 1989 (link)). Items cover lifetime propensities to experience depressive symptoms (e.g., “Have you become sad, depressed, or irritable for several days or more without really understanding why?”), and hypomanic symptoms (e.g., “Have there been periods of several days or more when your thinking was so clear and quick that it was much better than most other people's?”). Responses are rated on a four-point Likert scale ranging from “never or hardly ever” to “very often or almost constantly”. The GBI also includes some Biphasic items to capture tendencies for mood states to vary from extremely high to extremely low. Biphasic and Hypomania items are commonly collapsed into a single scale (the Hypomanic/Biphasic, or Mania scale) which prospectively predicts onset of manic episodes (Alloy et al., 2012 (link)). The GBI Mania and Depressive scale scores display sound psychometric properties across multiple samples including internal reliability alphas exceeding .90, test-retest reliabilities exceeding .70, strong predictive validity, and adequate convergent and discriminant validity across multiple samples (reviewed in Johnson et al., 2008 ; Youngstrom, 2007 ). Exploratory factor analyses typically find two strong factors of depression and hypomanic/biphasic mood, along with various small factors that capture less variance and are not typically scored separately (e.g., Depue et al., 1981 (link); Murray, Goldstone, & Cunningham, 2007).
Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime version (K-SADS-PL;Kaufman et al., 1997 (link)). The KSADS is a commonly used, well-validated interview for establishing bipolar diagnoses among youth. Youths and primary caregivers completed KSADS interviews in the youth samples. Raters were highly trained and inter-rater reliability was sustained throughout the study (kappa for symptom severity ≥ .85 in both samples). Diagnoses were reviewed by either a board certified child psychiatrist or licensed clinical psychologist. Diagnoses conformed to strict DSM-IV criteria for bipolar I, bipolar II, cyclothymic disorder, and bipolar NOS (typically due to insufficient duration of the index hypo/manic episode).
The youth samples included the Young Mania Rating Scale (YMRS, Young, Briggs, & Meyer, 1978 (link)), which has good inter-rater reliability, correlates with other manic severity measures, and is sensitive to treatment effects; and the Child Depression Rating Scale – Revised (CDRS, Poznanski, Miller, Salguero, & Kelsh, 1984 (link)) to quantify depressive symptom severity. In the youth samples, the primary caregiver completed the Parent-report GBI (P-GBI, Youngstrom, Findling, Danielson, & Calabrese, 2001 (link)) and the Internalizing Problems score on the Child Behavior Checklist (CBCL; Achenbach & Rescorla, 2001 ) about the youth's mood traits, providing a cross-informant perspective.
The Mood Disorder Questionnaire (MDQ: Hirschfeld et al., 2000 (link)) is a 15-item self-report measure of hypomanic symptoms: 13 yes/no items cover DSM manic symptoms, the 14th item asks about simultaneous occurrence, and the last item rates impairment. We used a threshold of 7 or more symptoms co-occurring at least once (Miller, Johnson, Kwapil, & Carver, 2011 (link)). The TEMPS-A measured Affective Temperaments, with five rationally derived subscales: Dysthymic, Cyclothymic, Hyperthymic, Irritable, and Anxious Temperament (published alphas from 0.67 to 0.91, Akiskal, Akiskal, Haykal, Manning, & Connor, 2005 (link)). Hyperthymic scales differentiate BD from other mood disorders, and Dysthymic temperament predicts the severity of depression within BD (e.g., Karam et al., 2010 (link)). The 5-item Satisfaction with Life scale (SWL, Diener, Emmons, Larsen, & Griffin, 1985 (link); Diener, Suh, Lucas, & Smith, 1999 ) measured Satisfaction with Life, as both manic and depressive traits are associated with low life satisfaction (Freeman et al., 2009 (link); Murray & Michalak, in press ). BD is associated with an evening chronotype (Wood et al., 2009 (link)) and elevated seasonal variation in mood and behavior (Shin, Schaffer, Levitt, & Boyle, 2005). The Morningness-Eveningness Questionnaire (MEQ, Horne & Ostberg, 1976 (link)) measured chronotype; higher scores indicate greater morningness. The Seasonal Pattern Assessment Questionnaire (SPAQ, Rosenthal, Bradt, & Wehr, 1984 ) assessed seasonality. Creativity has reliable associations with bipolar diagnosis (Murray & Johnson, 2010 (link)). The 90-item Creative Behavior Inventory (CBI, Hocevar, 1979 ) quantified creative products generated during adolescence and adulthood. Respondents rated the frequency of creative behaviors since adolescence on a 4-point scale from “Never” to “5 or more times.”
Publication 2013
Adult Alloys Child Chronotype Creativity Cyclothymic Disorder Depressive Symptoms Diagnosis Disorder, Attention Deficit-Hyperactivity Disruptive Behavior Disorder Epistropheus Mania Manic Episode Mood Mood Disorders Parent Population Group Psychiatrist Psychologist Psychometrics Sadness Satisfaction Schizophrenia, Childhood Sound Student Syndrome Temperament Unipolar Depression Youth
Prospective onsets of mood episodes were assessed with the exp-SADS-C diagnostic interview administered face-to-face approximately every 4 months during the follow-up; 87.3% of the 4-month assessments occurred on schedule. Interviews that could not be completed face-to-face were completed by phone (approximately 15% were completed by phone). The exp-SADS-C is a semistructured diagnostic interview that assesses prospective changes in severity, duration, and number of clinical symptoms and allows for diagnosis of onsets, remissions, relapses, and recurrences of disorders covered by the interview. It was expanded in the same ways as the exp-SADS-L. In addition, features of the Longitudinal Interval Follow-up Evaluation (LIFE II; Shapiro & Keller, 1979) were added to the exp-SADS-C to systematically track the course of symptoms and episodes during follow-up. However, the exp-SADS-C inquired about the presence of each symptom of depression and hypomania/mania more frequently (daily) than does the LIFE II (weekly) during the 4-month interval. Exp-SADS-C interviewers were blind to participants’ diagnostic information at the Stage II screening, family history, age at onset, and BAS and impulsivity scores. Like the exp-SADS-L interviews, exp-SADS-C diagnoses were based on consensus of interviewers and senior diagnosticians. In addition, all ambiguous exp-SADS-C interviews and a random 10% of other interviews were reviewed by the expert psychiatric diagnostic consultant. Joint ratings of 60 exp-SADS-C interviews for the LIBS Project yielded good inter-rater reliability (for Bipolar I, κ = 1.0, for Bipolar II, κ = .92, for Cyclothymia/BiNOS, κ = .88). Inter-rater reliability for individual symptom ratings were r’s = .93 for both hypomanic and depressive symptoms. Moreover, a validity study indicated that interviewers dated symptoms on the exp-SADS-C with at least 70% accuracy compared to participants’ daily symptom ratings made over a 4-month interval (Alloy et al., 2008 (link)). The exp-SADS-C also provided information about participants’ treatment seeking (medication or psychotherapy) during each 4-month interval since the previous assessment.
Manic and mixed episodes met relevant DSM-IV-TR criteria, including number and duration of symptoms. All manic and mixed episodes were characterized by either hospitalization, presence of psychotic symptoms, and/or grave impairment (e.g., serious legal consequences stemming from risky behavior during manic episode). Details of the criteria used to diagnose bipolar mood episodes on the exp-SADS-C interviews are provided in the appendix.
Publication 2011
Alloys Blindness Consultant Cyclothymic Disorder Depressive Symptoms Diagnosis Face Hospitalization Interviewers Joints Mania Manic Episode Mental Disorders Mood Pharmaceutical Preparations Psychotherapy Recurrence Relapse Sadness

Most recents protocols related to «Alloys»

Example 1

95 g of manganese (purity: 99.95%; purchased from Taewon Scientific Co., Ltd.) and 5 g of high-purity graphite (purity: 99.5%; purchased from Taewon Scientific Co., Ltd.) were placed in a water-cooled copper crucible of an argon plasma arc melting apparatus (manufactured by Labold AG, Germany, Model: vacuum arc melting furnace Model LK6/45), and melted at 2,000 K under an argon atmosphere. The melt was cooled to room temperature at a cooling rate of 104 K/min to obtain an alloy ingot. The alloy ingot was crushed to a particle size of 1 mm or less by hand grinding. Thereafter, the obtained powders were magnetically separated using a Nd-based magnet to remove impurities repeatedly, and the Mn4C magnetic powders were collected. The collected Mn4C magnetic powders were subjected to X-ray diffraction (XRD) analysis (measurement system: D/MAX-2500 V/PO, Rigaku; measurement condition: Cu—Kα ray) and energy-dispersive X-ray spectroscopy (EDS) using FE-SEM (Field Emission Scanning Electron Microscope, MIRA3 LM).

FIGS. 2(a) and 2 (b) show an X-ray diffraction pattern and an energy-dispersive X-ray spectroscopy graph of the Mn4C magnetic material produced according to Example 1 of the present disclosure, respectively.

As can be seen in FIG. 2(a), the Mn4C magnetic material showed diffraction peaks of (111), (200), (220), (311) and (222) crystal planes at 2θ values of 40°, 48°, 69°, 82° and 88°, respectively, in the XRD analysis. Thus, it can be seen that the XRD patterns of the Mn4C magnetic material produced according to Example 1 are well consistent with the patterns of the cubic perovskite Mn4C. In addition, the Mn4C magnetic material shows several very weak diffraction peaks that can correspond to Mn23C6 and Mn. That is, the diffraction peak intensity at 2θ values of 43° and 44°, which correspond to Mn and Mn23C6 impurities, is as very low as about 2.5% of the diffraction intensity of the peak corresponding to the (111) plane. Through this, it can be seen that the powders obtained in Example 1 have high-purity Mn4C phase. The lattice parameter of the Mn4C is estimated to be about 3.8682 Å.

FIG. 2(b) shows the results of analyzing the atomic ratio of Mn:C in the powder by EDS. The atomic ratio of Mn:C is 80.62:19.38, which is very close to 4:1 within the experimental uncertainties. Thus, it can be seen that the powder is also confirmed to be Mn4C.

The M-T curve of the field aligned Mn4C powder obtained in Example 1 was measured under an applied field of 4 T and at a temperature ranging from 50 K to 400 K. Meanwhile, the M-T curve of the randomly oriented Mn4C powder was measured under an applied field of 1 T. The Curie temperature of Mn4C was measured under 10 mT while decreasing temperature from 930 K at a rate of 20 K/min.

FIGS. 3(a) to 3(c) show the M-T curves of the Mn4C magnetic material, produced according to Example 1 of the present disclosure, under magnetic fields of 4 T, 1 T, and 10 mT, respectively.

FIG. 3 shows magnetization-temperature (M-T) curves indicating the results of measuring the temperature-dependent magnetization intensity of the Mn4C magnetic material, produced in Example 1, using the vibrating sample magnetometer (VSM) mode of Physical Property Measurement System (PPMS®) (Quantum Design Inc.).

According to the Néel theory, the ferrimagnets that contain nonequivalent substructures of magnetic ions may have a number of unusual forms of M-T curves below the Curie temperature, depending on the distribution of magnetic ions between the substructures and on the relative value of the molecular field coefficients. The anomalous M-T curves of Mn4C, as shown in FIG. 3(a), can be explained to some extent by the Néel's P-type ferrimagnetism, which appears when the sublattice with smaller moment is thermally disturbed more easily. For Mn4C with two sublattices of MnI and MnII, as shown in FIG. 1, the MnI sublattice might have smaller moment.

FIG. 3(a) shows the temperature dependence of magnetization of the Mn4C magnetic material produced in Example 1. The magnetization of Mn4C measured at 4.2K is 6.22 Am2/kg (4 T), corresponding to 0.258μB per unit cell. The magnetization of the Mn4C magnetic material varies little at temperatures below 50 K, and is quite different from that of most magnetic materials, which undergo a magnetization deterioration with increasing temperature due to thermal agitation. Furthermore, the magnetization of the Mn4C magnetic material increases linearly with increasing temperature at temperatures above 50 K. The linear fitting of the magnetization of Mn4C at 4 T within the temperature range of 100 K to 400 K can be written as M=0.0072T+5.6788, where M and T are expressed in Am2/kg and K, respectively. Thus, the temperature coefficient of magnetization of Mn4C is estimated to be about ˜2.99*10−4μB/K per unit cell. The mechanisms of the anomalous thermomagnetic behaviors of Mn4C may be related to the magnetization competition of the two ferromagnetic sublattices (MnI and MnII) as shown in FIG. 1.

FIG. 3(b) shows the M-T curves of the Mn4C powders at temperatures within the range of 300 K to 930 K under 1 T. The linear magnetization increment stops at 590 K, above which the magnetization of Mn4C starts to decrease slowly first and then sharply at a temperature of about 860 K. The slow magnetization decrement at temperatures above 590 K is ascribed to the decomposition of Mn4C, which is proved by further heat-treatment of Mn4C as described below.

According to one embodiment of the present disclosure, the saturation magnetization of Mn4C increases linearly with increasing temperature within the range of 50 K to 590 K and remains stable at temperatures below 50 K. The increases in anomalous magnetization of Mn4C with increasing temperature can be considered in terms of the Néel's P-type ferrimagnetism. At temperatures above 590 K, the Mn4C decomposes into Mn23C6 and Mn, which are partially oxidized into the manganosite when exposed to air. The remanent magnetization of Mn4C varies little with temperature. The Curie temperature of Mn4C is about 870 K. The positive temperature coefficient (about 0.0072 Am2/kgK) of magnetization in Mn4C is potentially important in controlling the thermodynamics of magnetization in magnetic materials.

The Curie temperature Te of Mn4C is measured to be about 870 K, as shown in FIG. 3(c). Therefore, the sharp magnetization decrement of Mn4C at temperatures above 860 K is ascribed to both the decomposition of Mn4C and the temperature near the Tc of Mn4C.

FIG. 4 is a graph showing the magnetic hysteresis loops of the Mn4C magnetic material, produced according to Example 1 of the present disclosure, at 4.2 K, 200 K and 400 K. The magnetic hysteresis loops were measured by using the PPMS system (Quantum Design) under a magnetic field of 7 T while the temperature was changed from 4 K to 400 K.

As shown in FIG. 4, the positive temperature coefficient of magnetization was further proved by the magnetic hysteresis loops of Mn4C as shown in FIG. 4. The Mn4C shows a much higher magnetization at 400 K than that at 4.2 K. Moreover, the remanent magnetization of Mn4C varies little with temperature and is Δ3.5 Am2/kg within the temperature range of 4.2 K to 400 K. The constant remanent magnetization of Mn4C within a wide temperature range indicates the high stability of magnetization against thermal agitation. The coercivities of Mn4C at 4.2 K, 200 K, and 400 K were 75 mT, 43 mT, and 33 mT, respectively.

The magnetic properties of Mn4C measured are different from the previous theoretical results. A corner MnI moment of 3.85μB antiparallel to three face-centered MnII moments of 1.23μB in Mn4C was expected at 77 K. The net moment per unit cell was estimated to be 0.16μB. In the above experiment, the net moment in pure Mn4C at 77 K is 0.26μB/unit cell, which is much larger than that expected by Takei et al. It was reported that the total magnetic moment of Mn4C was calculated to be about 1μB, which is almost four times larger than the 0.258μB per unit cell measured at 4.2 K, as shown in FIG. 4.

FIG. 5 is an enlarged view of the temperature-dependent XRD patterns of the Mn4C magnetic material produced according to Example 1 of the present disclosure.

The thermomagnetic behaviors of Mn4C are related to the variation in the lattice parameters of Mn4C with temperature. It is known that the distance of near-neighbor manganese atoms plays an important role in the antiferro- or ferro-magnetic configurations of Mn atoms. Ferromagnetic coupling of Mn atoms is possible only when the Mn—Mn distance is large enough. FIG. 5 shows the diffraction peaks of the (111) and (200) planes of Mn4C at temperatures from 16 K to 300 K. With increasing temperature, both (111) and (200) peaks of Mn4C shifted to a lower degree at temperatures between 50 K and 300 K, indicating an enlarged distance of Mn—Mn atoms in Mn4C. No peak shift is obviously observed for Mn4C at temperatures below 50 K. The distance of nearest-neighbor manganese atoms plays an important role in the antiferro- or ferro-magnetic configurations of Mn atoms and thus has a large effect on the magnetic properties of the compounds.

Thus, it can be seen that the abnormal increase in magnetization of Mn4C with increasing temperature occurs due to the variation in the lattice parameters of Mn4C with temperature.

The powder produced in Example 1 was annealed in vacuum for 1 hour at each of 700 K and 923 K, and then subjected to X-ray spectroscopy, and the results thereof are shown in FIG. 6.

The magnetization reduction of Mn4C at temperatures above 590 K is ascribed to the decomposition of Mn4C, which is proved by the XRD patterns of the powders after annealing Mn4C at elevated temperatures. FIG. 6 shows the structural evolution of Mn4C at elevated temperatures. When Mn4C is annealed at 700 K, a small fraction of Mn4C decomposes into a small amount of Mn23C6 and Mn. The presence of manganosite is ascribed to the spontaneous oxidation of the Mn precipitated from Mn4C when exposed to air after annealing. The fraction of Mn23C6 was enhanced significantly for Mn4C annealed at 923 K, as shown in FIG. 6.

These results prove that the metastable Mn4C decomposes into stable Mn23C6 at temperatures above 590 K. The presence of Mn4C in the powder annealed at 923 K indicates a limited decomposition rate of Mn4C, from which the Tc of Mn4C can be measured. Both Mn23C6 and Mn are weak paramagnets at ambient temperature and elevated temperatures. Therefore, the magnetic transition of the Mn4C magnetic material at 870 K is ascribed to the Curie point of the ferrimagnetic Mn4C.

The Mn4C shows a constant magnetization of 0.258μB per unit cell below 50 K and a linear increment of magnetization with increasing temperature within the range of 50 K to 590 K, above which Mn23C6 precipitates from Mn4C. The anomalous M-T curves of Mn4C can be considered in terms of the Néel's P-type ferrimagnetism.

Patent 2024
Alloys Argon Atmosphere Biological Evolution Cells Copper Cuboid Bone Debility Energy Dispersive X Ray Spectroscopy Face Fever fluoromethyl 2,2-difluoro-1-(trifluoromethyl)vinyl ether Graphite Ions Magnetic Fields Manganese perovskite Physical Processes Plasma Powder Radiography Scanning Electron Microscopy Spectrum Analysis Vacuum Vision X-Ray Diffraction
Not available on PMC !

Example 3

FIG. 8 is a photograph illustrating the microstructure of an example alloy composition including an iron nitride foil, with an average grain size of 8±1.5 μm. The example alloy composition of FIG. 8 was prepared using melt spinning. The composition had a coercivity of 200 Oe. The grains were relatively large, and ferromagnetically coupled.

Example 4

FIG. 9 is a photograph illustrating the microstructure of an example alloy composition including an iron nitride foil, with an average grain size of 6±1.3 μm. The example alloy composition of FIG. 9 was prepared using melt spinning. The composition had a coercivity of 2037 Oe. The grain boundaries were thicker compared to the grains of the example alloy composition of Example 3, and the ferromagnetic grains were separated by non-magnetic material.

Patent 2024
Alloys Cereals Iron
Not available on PMC !

Example 6

A sample alloy composition including a plurality of grains with a predetermined average grain size may be prepared according to the present prophetic example. Pure iron foil, for example, having a thickness of between about 1 μm to about 1 cm, is used as a precursor. The iron foil is heated at a temperature between about 650° C. and about 1600° C. for a period of time between about 0.5 hours and about 10 hours, followed by quenching in a liquid medium. The liquid medium includes cold water, brine, oil, liquid nitrogen, or liquid CO2. A grain structure associated with an average grain size between about 20 nm and about 100 nm is formed. The sample is nitrided using ammonia, at a temperature between about 120° C. and about 500° C., subject to a pressure between about 1 atmosphere and about 100 atmospheres. The nitride sample is annealed by a strained workpiece technique.

Patent 2024
Alloys Ammonia Atmosphere Atmospheric Pressure brine Cereals Cold Temperature Iron Nitrogen
Not available on PMC !

Example 2

Suppose there is a qualitative distributed parameter model (represented by Tonti diagrams) of a 3D metallic box 910 made of aluminum alloy with cracks 920, as shown in FIG. 9. By adding a wave source 930 at the center of one surface of the 3D metallic box 910, after the qualitative simulation, it is possible to find the position of the cracks 920 by checking at what positions there is a sudden change of the wave amplitudes. Such changes are not only a function of time but also a function of space. FIGS. 14A and 14B show a Tonti diagram that may be used for this example.

Patent 2024
Alloys Aluminum Figs Metals
Not available on PMC !

Example 4

The reaction inhibitor can be used to render inactive the lithium or lithium-based alloy contained in a primary or secondary battery, in order to recover commercially valuable materials and/or to recycle them. For example, the recycling process comprises shredding batteries by grinding in presence of an aqueous solution containing the organic inhibitor. When using this process lithium oxidizes in aqueous solution to form LiOH. Once the lithium is completely dissolved in this form, the shredded (and non-reactive) materials are rinsed with water to remove traces of LiOH. The lithium can then be recycled from the aqueous solution of LiOH in the form of LiOH·H2O or converted in the form of Li2CO3 or another lithium salt. These compounds can then be reused for the production of electrochemically active materials such as, for example, LiFePO4, Li4Ti5O12, or metallic lithium, or for the production of lithium salts used in the manufacture of liquid, solid or gel electrolytes.

Patent 2024
Alloys Electrolytes LiFePO4 Lithium Lithium-4 Metals Salts

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More about "Alloys"

Metallic Materials, Compound Metals, Metal Blends, Metallurgical Compositions, Engineered Metals, Composite Metals, Alloyed Materials, Metal Amalgams.
Alloys are versatile and widely-used engineered materials composed of two or more chemical elements, at least one of which is a metal.
These composite metals are designed to exhibit enhanced properties such as increased strength, corrosion resistance, or thermal conductivity compared to their individual components.
Alloys are crucial in a variety of industries including aerospace, automotive, construction, and electronics.
Researchers continually explore new alloy compositions and production methods, such as leveraging advanced analytical tools like the S-4800 and D8 Advance scanning electron microscopes, the FBS and JSM-5910 SEMs, the Vitrobot Mark IV for sample preparation, and the JEM-2100F and JEM-2100 transmission electron microscopes, to optimize alloy performance and cost-effectiveness.
The JSM-7800F and Ultima IV SEMs also play a key role in alloy characterization.
PubCompare.ai revolutionizes this process by using AI to identify the best alloy protocols and products for specific applications, enhancing reproducibility and accuracy in alloy research and development.