18,565 research outputs found
Ga-Substituted Nanoscale HZSM‑5 in Methanol Aromatization: The Cooperative Action of the Brønsted Acid and the Extra-Framework Ga Species
No full textA series of nanosized [Al]-, [Ga,
Al]-, and [Ga]-HZSM-5 catalysts with a fixed Si-to-M3+ ratio
(M = Al or Ga) was synthesized by a seed-induced crystallization method.
In order to reveal the catalytic nature of the extra-framework Ga
species, an acid treatment was applied to selectively extract Lewis
acidic amorphous Ga cations of as-synthesized catalysts. A comparative
evaluation of freshly prepared and acid-treated catalysts in methanol
conversion to aromatics showed the dehydrogenative nature of the extra-framework
Ga species, which is essential in the enhancement of aromatics. Among
tested catalysts, [Ga, Al]-HZSM-5 with a low extra-framework Ga-to-Brønsted
acid ratio (0.06) was the most effective. The efficacy is due to the
contact synergy of the extra-framework Ga species and the Brønsted
acid, by which aromatics originated from the dual-cycle mechanism
of methanol conversion can be accelerated
Magnetic properties of (Ga,Mn)As (110) epitaxial films
No full textMagnetic properties of (Ga,Mn)As films epitaxied on GaAs (110) substrates have been investigated. Both magnetic and magnetotransport measurements indicate dominant in-plane magnetic anisotropies for as-grown and annealed samples. Moreover, obvious in-plane spin reorientation transition upon the change of temperature has been observed for the as-grown samples, which disappears after annealing. The above phenomena are shown to be correlated with the competition between the cubic and the uniaxial magnetic anisotropic fields. The relative strengths of these two terms are quantitatively obtained by the planar Hall measurements and can be tuned by annealing. For all the annealed (Ga,Mn)As (110) films, a dominant [−110] uniaxial magnetic easy axis is found and the mechanism is discussed. Our work provides useful information for understanding the origin of magnetic anisotropies in (Ga,Mn)As films
Improved tunneling magnetoresistance in (Ga,Mn)As/AlOx/CoFeB magnetic tunnel junctions
No full text<p>We fabricated (Ga,Mn)As/AlOx/Co40Fe40B20 magnetic tunnel junctions with ferromagnetic semiconductor/insulator/ferromagnetic metal (S/I/F) structure. The treatments of pre-annealing and post-plasma cleaning on the (Ga,Mn) As film were introduced before the growth of the subsequent layers. A high tunneling magnetoresistance (TMR) ratio of 101% is achieved at 2 K, and the spin polarization of (Ga,Mn) As, P = 56.8%, is deduced from Julliere's formula. The improved TMR ratio is primarily due to the improved magnetism of (Ga,Mn) As layer by low-temperature annealing and cleaned interface between (Ga,Mn) As and AlOx attained by subsequent plasma cleaning process.</p
Improved tunneling magnetoresistance in (Ga,Mn)As/AlO(x)/CoFeB magnetic tunnel junctions
No full textWe fabricated (Ga,Mn)As/AlO(x)/Co(40)Fe(40)B(20) magnetic tunnel junctions with ferromagnetic semiconductor/insulator/ferromagnetic metal (S/I/F) structure. The treatments of pre-annealing and post-plasma cleaning on the (Ga,Mn) As film were introduced before the growth of the subsequent layers. A high tunneling magnetoresistance (TMR) ratio of 101% is achieved at 2 K, and the spin polarization of (Ga,Mn) As, P = 56.8%, is deduced from Julliere's formula. The improved TMR ratio is primarily due to the improved magnetism of (Ga,Mn) As layer by low-temperature annealing and cleaned interface between (Ga,Mn) As and AlO(x) attained by subsequent plasma cleaning process. (C) 2011 American Institute of Physics. [doi:10.1063/1.3603946
GA-Faster-RCNN network architecture.
No full textIndustrial defect detection is a critical aspect of production. Traditional industrial inspection algorithms often face challenges with low detection accuracy. In recent years, the adoption of deep learning algorithms, particularly Convolutional Neural Networks (CNNs), has shown remarkable success in the field of computer vision. Our research primarily focused on developing a defect detection algorithm for the surface of Flexible Printed Circuit (FPC) boards. To address the challenges of detecting small objects and objects with extreme aspect ratios in FPC defect detection for surface, we proposed a guided box improvement approach based on the GA-Faster-RCNN network. This approach involves refining bounding box predictions to enhance the precision and efficiency of defect detection in Faster-RCNN network. Through experiments, we verified that our designed GA-Faster-RCNN network achieved an impressive accuracy rate of 91.1%, representing an 8.5% improvement in detection accuracy compared to the baseline model.</div
Dominant recombination centers in Ga(In)NAs alloys: Ga interstitials
No full textOpticallydetected magnetic resonance measurements are carried out to study formationof Ga interstitial-related defects in Ga(In)NAs alloys. The defects, whichare among dominant nonradiative recombination centers that control carrier lifetimein Ga(In)NAs, are unambiguously proven to be common grown-in defectsin these alloys independent of the employed growth methods. Thedefects formation is suggested to become thermodynamically favorable because ofthe presence of nitrogen, possibly due to local strain compensation.Original Publication: Xingjun Wang, Yuttapoom Puttisong, C. W. Tu, Aaron J. Ptak, V. K. Kalevich, A. Yu. Egorov, L. Geelhaar, H. Riechert, Weimin Chen and Irina Buyanova, Dominant recombination centers in Ga(In)NAs alloys: Ga interstitials, 2009, Applied Physics Letters, (95), 241904. http://dx.doi.org/10.1063/1.3275703 Copyright: American Institute of Physics http://www.aip.org
Solution-processed Ga-Cd-O thin-films with tunable bandgaps and their transistors
No full textGa-Cd-O (GCO) thin films with different Ga contents were fabricated based on a solution-processed method. The direct optical bandgap of GCO films is changed from 2.89 to 4.53 eV with the Ga-content from 30% to 100% and their relationship agrees well with a second-order equation. The Raman spectra of GCO are dominated by three main features: a relatively sharp peak at similar to 260 cm(-1) and two broad features at similar to 405 and 949 cm(-1), and their variations with the Ga-content are analyzed associated with their phonon mode assignment. Moreover, thin-film transistors using the GCO channels all exhibit n-type transistor characteristics and the evolution of their key electrical parameters with the Ga-content is well elucidated by the structural and morphological properties. A saturation field-effect mobility of 5.1 cm(2) V-1 s(-1), on/off current ratio of 2.1 x 10(7), subthreshold slop of 0.83 V/dec, and threshold voltage of -6.8 V were achieved by the 50% Ga-content device
GA-Fuzzy PID control simulation waveform diagram.
No full textAs is well known, the metal annealing process has the characteristics of heat concentration and rapid heating. Traditional vacuum annealing furnaces use PID control method, which has problems such as high temperature fluctuation, large overshoot, and long response time during the heating and heating process. Based on this situation, some domestic scholars have adopted fuzzy PID control algorithm in the temperature control of vacuum annealing furnaces. Due to the fact that fuzzy rules are formulated through a large amount of on-site temperature data and experience summary, there is a certain degree of subjectivity, which cannot ensure that each rule is optimal. In response to this drawback, the author combined the technical parameters of vacuum annealing furnace equipment, The fuzzy PID temperature control of the vacuum annealing furnace is optimized using genetic algorithm. Through simulation and comparative analysis, it is concluded that the design of the fuzzy PID vacuum annealing furnace temperature control system based on GA optimization is superior to fuzzy PID and traditional PID control in terms of temperature accuracy, rise time, and overshoot control. Finally, it was verified through offline experiments that the fuzzy PID temperature control system based on GA optimization meets the annealing temperature requirements of metal workpieces and can be applied to the temperature control system of vacuum annealing furnaces.</div
Performances of crosslinked asymmetric poly(vinyl alcohol) membranes for isopropanol dehydration by pervaporation
No full textAsymmetric poly(vinyl alcohol) (PVA) membranes crosslinked with glutaraldehyde (GA) were prepared by phase inversion technique for pervaporation dehydration of isopropanol (IPA). The crosslinking solutions contain sodium sulfate, sulfuric acid and different contents of GA, wherein sulfuric acid serves as a catalyst. The effects of three variables involved in the membrane preparation, including PVA molecular weight, PVA concentration and GA concentration on pervaporation characteristics were investigated. The results showed that the permeation flux decreases with the increase of these three factors, whereas the selectivity has an opposite trend. The influence of feed IPA concentration and temperature on pervaporation performances was also studied to find optimum operating conditions. (C) 2002 Elsevier Science B.V. All rights reserved.This work was supported by LG Chem. Ltd/BK 21 project. The authors wish to express their thanks. Dr Yu is also grateful to the Korea Foundation for Advanced Studies for a fellowship
KK1-2693 - Wa hte yu (Pig and rat) with transcription
No full textTranscription (Ja Seng Roi) Ya nga i hkai na gaw Wa hteYu. Moi shawng de da wa gaw i wa lawng kaw rawng taw da. Rawng taw shaloi wo ra wa Yu wa, Yu ngai hpe hpaw la rit, hpaw la rit ngu n hpaw la lu na, wo ra, wo ra n hkyi hte kalap n hpaw la lu na shan hkawng arau wam hkawm re arau arau bum de n arau wawm hkawm na swi tawm re na an hte gaw dai ni, dai ni pyaw sha i taw kade pyaw sha shadu nla..... ko ya an hkawng wo hka de naw sa ga i, hka de. Hka ja la na bum de sa wa na. Bum de hka lu la rai na wo wu koi dung taw rai na nsin nsiin ai shana n ga wa wa ga i. Um nga hte hka hte hpa hte swi ya an hte a kao n sin sin wa jan du wa yang gaw wa wa ga i. wa ga. wa sai da. N re wa wa reng gaw dai kaw nsin n sin marang htu katut na langa rau, n re wo ra hpun lap rau dai rau gup na wa wa reng, sha na nsin yup taw, yup taw re jahpawt du sai, jahpawt du kawn bai, bai sa bai sa rai kaw si wa ngan shat bai wa sha la na bai wa sa re shaloi koi aw e Bum de wa deng, bum de wa grai tsan ai i lam rau nga yang grai ni nga le i nga na lung wa re wa deng lung wa shaloi wa deng shan na nta yu dat ai shaloi grai tsan taw shaloi kwi gai tsan taw i nta kaw reng dung taw ga dai kaw ao hka de bai bai lu la, bai lu la lu la, lu la rai bai lu la rai n tsa bai lung wa ao yat nang ma muk bai hpyi hpyi rai ya gumhpraw n'gun ai lu ya, muk mung nang de n dut ai lu e ya ndai gaw an hte ao ao nye ganau la e ma shaga la ga i nga hte shaga la swi an nau masum tem rai dai kaw nga taw shaloi aw n re oi e wo ra wa nang sa wa lo nga hte e lo ya ya jahkring yaw sa wa shaloi wa wo lam madu ni wa deng grai grai kaba ngan wa saga lo kagat na yu wa reng yu wa reng ya an hte an hkawng gaw wa sana i n wa sana i?n wa shi ga law ya dai ni dai ni n grai pyaw ya hte an hkawng an hkawng mung ya she sa wa re ngang saw dai na bum kaw yup na i?yup ga Nta gap la ga i. Wa gaw n gap di na Wa gaw n gap di na rawng taw da. Yu wa gaw grai chye da, hpun de na gra gra di na galaw la na, galaw la loi wo ya yaw nang galoi ngut wo shi Nu Ah Nu nang galoi wa na rai, Wa e Wa nang galoi, hkau nang ma rai sai law hka lu jin jin rai na lu gaw n re hka gun nre shana du wa shaloi yup taw sai da. Yup yuo rai taw goi Jahpawt n ga de wa wa nta de, shan na nta de wa wa re hkoi dai kaw nga taw nga taw, nga taw re hpang jahpawt bum de sa yu nta wa nyawp rai taw n jup jup rai taw bai galaw la n na shana bai yup taw, shana du wa yup re sai jahpat de nta bai wa re hkoi jahpawt gaw da Jahpawt gaw da yawng hten mat na n ta de wa yu n me gai pyaw taw ai da. . Language as given: Jinghpa
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