Flow chart for the synthesis of Sr2FeMoO6 compound.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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Since the first years of the introduction of cocaine by Carl Koller in 1884, the evolution of regional anesthesia has been continuous, gradual and safe. Its development has been based on anatomy, the pharmacology of local anesthetics and adjuvant drugs, as well as advances in the various blocking techniques, with ultrasound guidance being the most recent advent. The use of ultrasound in regional anesthesia has shown the reduction of complications, which makes it mandatory to knowledge and acquire skills in all ultrasound-guided techniques.
\r\n\r\n\tUltrasound-guided regional blocks will be reviewed extensively, as well as intravenous regional anesthesia, thoracic spinal anesthesia. The role of regional anesthesia and analgesia in critically ill patients is of paramount importance. In addition, we will review the current role of regional techniques during the Covid-19 pandemic. Complications and malpractice is another topic that should be reviewed. Regional anesthesia procedures in some specialties such as pediatrics, orthopedics, cancer surgery, neurosurgery, acute and chronic pain will be discussed.
",isbn:"978-1-83969-570-4",printIsbn:"978-1-83969-569-8",pdfIsbn:"978-1-83969-571-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"264f7f37033b4867cace7912287fccaa",bookSignature:"Prof. Víctor M. Whizar-Lugo, Dr. José Ramón Saucillo-Osuna and Dr. Guillermo A. Castorena-Arellano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10708.jpg",keywords:"Regional Anesthesia, Ultrasound-Guided Regional Anesthesia, Local Anesthetics, Preventive Analgesia, Peripheral Blocks, Pediatric Regional Anesthesia, Intravenous Regional Anesthesia, Techniques, Complications, Adjuvants in Regional Anesthesia, Opioids, Alfa2 Agonists",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 25th 2021",dateEndSecondStepPublish:"March 25th 2021",dateEndThirdStepPublish:"May 24th 2021",dateEndFourthStepPublish:"August 12th 2021",dateEndFifthStepPublish:"October 11th 2021",remainingDaysToSecondStep:"23 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Whizar-Lugo has published more than 100 publications on Anesthesia, Pain, Critical Care, and Internal Medicine. He works as an anesthesiologist at Lotus Med Group and belongs to the Institutos Nacionales de Salud as an associated researcher.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"169249",title:"Prof.",name:"Víctor M.",middleName:null,surname:"Whizar-Lugo",slug:"victor-m.-whizar-lugo",fullName:"Víctor M. Whizar-Lugo",profilePictureURL:"https://mts.intechopen.com/storage/users/169249/images/system/169249.jpg",biography:"Víctor M. Whizar-Lugo graduated from Universidad Nacional Autónoma de México and completed residencies in Internal Medicine at Hospital General de México and Anaesthesiology and Critical Care Medicine at Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán in México City. He also completed a fellowship at the Anesthesia Department, Pain Clinic at University of California, Los Angeles, USA. Currently, Dr. Whizar-Lugo works as anesthesiologist at Lotus Med Group, and belongs to the Institutos Nacionales de Salud as associated researcher. He has published many works on anesthesia, pain, internal medicine, and critical care, edited four books, and given countless conferences in congresses and meetings around the world. He has been a member of various editorial committees for anesthesiology journals, is past chief editor of the journal Anestesia en México, and is currently editor-in-chief of the Journal of Anesthesia and Critical Care. 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Dr. Saucillo-Osuna has lectured at multiple national and international congresses and is an adjunct professor at the Federación Mexicana de Colegios de Anestesiología, AC, former president of the Asociación Mexicana de Anestesia Regional, and active member of the Asociación Latinoamericana de Anestesia Regional.",institutionString:"Asociación Latinoamericana de Anestesia Regional",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:{id:"346513",title:"Dr.",name:"Guillermo A.",middleName:null,surname:"Castorena-Arellano",slug:"guillermo-a.-castorena-arellano",fullName:"Guillermo A. 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Spintronics materials have the unique possibilities for use in new functional microelectronic devices and adequate potential to become the ideal memory media for computing and it is a step in the direction of quantum computing due to its advancement in nonvolatility and magnetic random access memory (MRAM). This technology could also be used to create electronic devices, which are smaller, faster and consume less power. Spintronics is one of the emerging technology, which has extended the Moore’s law and industry is trying to put more than Moore. Any technology can replace the current world of electronics if it reduces any one of the very large scale integration (VLSI) cost functions like area, power consumption, speed, etc. Fortunately, spintronics can reduce heat dissipation significantly. In charge-based device to switch from logic “0” to logic “1,” the magnitude of the charge must be changed in the active region of the device due to which current flows from source to drain. It is not possible with charge-based electronics to reduce the power (or heat) dissipation, since charge is a scalar quantity and the presence or absence of charge gives logic “1” or logic “0.” Therefore, to meet the objective scientific community is developing the novel kind of materials that relies on magnetism instead of the flow of current through electron. The first widely acknowledged breakthrough in spintronics was the exploitation of giant magnetoresistance (GMR), a technology, which is now employed in the read heads of most hard drives. The discovery of giant magnetoresistance (GMR) has been cited as the first demonstration of a spintronics application and has been awarded the Nobel Prize in Physics in 2007 [1, 2]. GMR is a quantum mechanical effect observed in thin film heterostructures formed by alternating ferromagnetic and nonmagnetic (NM) layers. When a magnetic field (H) is applied, the thickness of NM layer is chosen such that there is a change in the direction of magnetization in another layer, which reflects a huge change in resistance. That is why the effect is called GMR, a large change in electrical resistance in presence of a magnetic field [2–4]. Baibich et al. represented the GMR of Fe/Cr magnetic superlattices by varying the magnetic field, thickness of NM (Cr) layer and by varying the number of superlattice structure [2]. They reported a large change in resistance or resistivity by applying the small magnetic field, which has a wide application in designing MRAM memories, magnetic read heads, MEMS device, etc. After that, a lot of experiments have been carried out using polarized neutron reflectometry (PNR) tool which clearly illustrates Fe/Cr superlattices that led to GMR effect [5] but PNR has some limitations also [6]. First magnetic sensor using GMR was released in 1994 [7], later, IBM produced the first GMR-read heads for reading data stored in magnetic hard disks [8, 9]. The first GMR-based RAM chips were produced by Honeywell in 1997. Today, GMR-based read heads are frequently used in laptops/computers, iPods, CD/DVD player, and other portable devices. In the twenty-first century, tunneling magnetoresistance (TMR)-based read heads began to displace GMR-based read heads. MRAM chips based on TMR devices are now marketed by several companies, such as Freescale, SanDisk, etc. Current efforts in designing and manufacturing spintronics devices is to optimize the existing GMR-based technology by either developing new materials with larger spin polarization of electrons or making improvements or variations in the existing devices that allow for better spin filtering and try to find new ways to generate and utilize the spin-polarized currents. Till date, magnetic multilayers using giant magnetoresistance (GMR) and tunneling magnetoresistance (TMR) have dominated the data storage industry for many years using simple ferromagnetic metals such as iron and chromium. The investigation of manganite-based magnetic tunnel junctions (MTJs) has deepened our understanding of spin-polarized tunneling and the interface properties of these complex oxides. However, the initial hopes of using them for room-temperature spintronics applications have not been satisfied. Several attempts to replace current microelectronic devices with nanoscale devices have led to a search for new materials with multifunctional properties (multitasking materials that can be manipulated by independent sources). Therefore, many potential half-metallic materials have been predicted and investigated [10–19], although to date roadblocks have occurred in each case. Half-metallic materials are of great interest due to their wide variety of physical properties, including ferromagnetism, ferroelectricity, superconductivity, and many more. In the last few decades, there has been a spectacular enhancement in research activities related to doped manganites, sparked by the discovery of colossal magnetoresistance (CMR) in lanthanum-doped manganites such as La1−xCaxMnO3 [20]. The large CMR effect of the order of 103 percent is observed at large magnetic fields of several Tesla at low temperature. One of the first working devices using CMR materials was constructed by Sun et al. in 1996 [21]. That consists of two layers of ferromagnetic La0.67Ca0.33MnO3 compound, separated by a thin SrTiO3 spacer layer, which showed a resistance decreased by a factor of 2 in a field of less than 20 mT. However, the main disadvantage of this device was the grain-boundary assisted magnetoresistance properties or in ferromagnetic tunneling junctions is that the large magnetic field sensitivities are only achieved at low temperatures. Furthermore, the CMR effect vanishes far below room temperature due to their low Curie temperatures [21, 22], which make their integration to be difficult for room temperature spintronics applications. The CMR devices exploiting some of the transport properties of manganites close to room temperature have however been proposed, such as contactless potentiometers [23] or bolometers [24, 25], but none of these are strictly speaking spintronics devices. The remarkable magnetoresistive (MR) properties at low-temperature in half-metallic manganites soon motivated the search for new half-metals with higher Curie temperatures [26, 27]. Several high Curie temperature compounds have been predicted to be half-metallic in the 1980s, like semi-Heusler alloys (NiMnSb) [10, 28–31], full-Heusler alloys [32, 33], zinc-blende structure materials [34–36], magnetic oxides (e.g., rutile CrO2 [13, 37–40] and spinel Fe3O4 [41, 42]). However, the first spin-polarization measurements of the Fe3O4 and Heusler alloys complex structures were disappointing [43]. CrO2 has both good conductivity and high
Much effort for the discovery of new high-
Primarily, most of the research works on double perovskites Sr2FeMoO6 (SFMO) has been focused on bulk ceramic samples and aimed to understand their structural, magnetic, and magnetotransport properties, along with correlation among them. The magnetoresistive (MR) properties in double perovskite SFMO generally arises from spin-dependent scattering at the grain boundaries. The underlying conduction mechanism is electron tunneling across insulating grain boundaries. Such magnetoresistance is reported to exist in polycrystalline samples in a magnetic field (~0.1 T), which are considerably lower fields than those utilized for manganite-based devices. The large MR, relatively at smaller external magnetic fields and at room temperature, is required in double perovskites compounds from the applications point of view. A material such as SFMO that exhibits a large decrease in resistivity and magnetically order well above room temperature is necessary for the advancement of spintronic devices.
\nIn our studies, we had synthesized the polycrystalline Sr2FeMoO6 sample by conventional solid-state reaction method (shown in Table 1). In brief, the stoichiometric amounts of high purity oxides and carbonates, such as SrCO3, Fe2O3, and MoO3, were mixed thoroughly as per abovementioned formula and calcined at 900°C in Argon (Ar) for 10 h. The calcined powder were reground and pressed into thin pellets of uniform size and sintered at 1200°C for 10 h in a gas flow of 5% H2 and 95% Ar. The details of the have been provided in our earlier reports [49–52].
\nFlow chart for the synthesis of Sr2FeMoO6 compound.
Figure 1 shows the rietveld fitted X-ray diffraction patterns of the polycrystalline Sr2FeMoO6 sample, which confirm the phase purity of the samples without any observable impurity phases. All the observed peaks of double perovskite phase are clearly visible with significant presence of ordering (103, 211) peaks.
\nRietveld fitted X-ray diffraction pattern of the samples Sr2FeMoO6.
Figure 2 shows the scanning electron micrographs with elemental analysis done at two regions: one at the grain and another at the grain boundary, represented by A and B. The Fe/Mo content ratio calculated through EDS results are 94% at the grain and 93% at grain boundary for SFMO sample, which show the good correlation.
\nScanning electron micrographs of Sr2FeMoO6 sample. A and B represents the regions where EDS pattern has been recorded at the grain and grain boundaries.
Magnetoresistance versus applied magnetic field plots for Sr2FeMoO6 samples are shown in Figure 3 at 300 and 77 K, respectively. The MR values were calculated by using formula, MR = 100 × [
MR plots for the Sr2FeMoO6 at 300 and 77 K.
The observation of high LFMR in such double perovskite system is due to the optimization of the high spin polarization of the carriers and grain boundary adjustment. Recent studies propose a new type of MR, where the spin polarization of grain boundaries is more crucial than the bulk polarization of samples [53, 55, 56]. Furthermore, the grain boundaries are magnetically hard compared to bulk in such double perovskite, which provides a different magnetic switching field for magnetoresistance and magnetization. The different behavior of the curves for normalized (M/Ms)2 versus magnetic field (H) and normalized MR versus H can be observed as one of the indication of such magnetically hard nature of grain boundaries. In our pristine sample such MR does not follow (M/Ms)2, rather (M/Ms)2 versus H curve saturate sharply compared to MR versus H curve, which suggest a MR mechanism similar to as reported by Sarma et al. [53]. Moreover, we estimated the low field behavior of MR and coercive field from magnetization M (μB/f.u.) plot and found that the peak in MR is invariably several times larger than the value of coercive field (Hc) for pristine Sr2FeMoO6 samples as described by Sarma et al. [53, 56]. This suggests that intragrain properties are not the key determinants for this MR. A remarkable 11% magnetoresistance was observed at room temperature under the presence of low magnetic field (0.72 T). The achieved low-field magnetoresistance value in the sample may be due to the optimized synthesis conditions to get better intergranular tunneling through grain boundaries.
\nUltimately, if the bulk properties observed could be reproduced in thin films; industrially produced SFMO-based spintronic devices could become a reality. The most used and optimized method for the growth of SFMO thin film is pulsed laser deposition (PLD) [57–71], however due to inherent complexity of SFMO, its growth as a thin film, has proven to be an arduous task. A brief look through the vast array of SFMO thin film literature reveals that the growth conditions are still in need of perfection [72–83]. The substrate temperature, vacuum, oxygen partial pressure, and gas atmosphere vary from one reference to the next, making it difficult to ascertain the optimal set of growth conditions for SFMO thin films. In the very first study, Manako et al. [71] presented the effect of oxygen pressure and substrate temperature in order to get epitaxial SFMO on SrTiO3 substrates. The phase diagram reported by them demonstrated only a narrow range of oxygen pressures (10−5 to 10−6 Torr) and temperature higher than 900°C (which is not easy to access by PLD) lead to single-phase, good quality thin films. In contrast, Santiso et al. [72] also grown the SFMO thin films and studied the effect of growth conditions, but found the formation of secondary impurity phases at high growth temperatures. Their results showed that the growth of SFMO\nthin\nfilms at 950°C in ultra-high vacuum (pressure less than 10−8 mbar), metallic iron precipitates can form, whereas in a flow of oxygen (pressure of 10−6 mbar), iron oxides can be formed.
Another important factor that can affect crystalline quality of the SFMO films is the choice of substrate. The most admired and used substrate for growing SFMO films is SrTiO3 (100) due to the close lattice matching [71, 77, 81, 82]. However, possible presence of oxygen vacancies [74] and low-level magnetic impurities in the SrTiO3 substrate could lead to unusual electrical behavior, potentially causing difficulties in interpreting the data. Other substrates including MgO, LaAlO3 [74, 80, 82, 84], and NdGaO3 [85] have also been used to obtain epitaxial films of SFMO. There have been several studies probing the effects of lattice mismatch on the ordering and magnetization [78, 86]. Asano et al. [86] reported one of the highest magnetizations attained by depositing SFMO (via sputtering deposition) on a lattice matched buffer layer, Ba0.4Sr0.6TiO3, then on SrTiO3 to minimize the effect of 1% lattice mismatch between SrTiO3 and SFMO. A considerable increase in magnetization of 2.3–3.8 μB/f.u. and the reduction of the expanded SFMO out-of-plane lattice parameter were observed with the use of the buffer layer. When experiments with the same buffer layer were performed by Sanchez et al. using PLD, slightly lower saturation magnetization value ~3.2 μB/f.u. was obtained [78]. In contrast, Yin et al. [80] found their magnetic and transport properties to be independent of the substrates.
\nThe most important and challenging task is to attain good value of MR in SFMO thin films along with the structural and magnetic properties, which will make it ideal for spintronics and magnetoresistive sensors applications. However, epitaxial thin films of SFMO do not show a large MR effect due to lack of grain boundaries. As a means to obtain large MR effects, there have been attempts by varying the deposition conditions and/or the surface of substrates [63, 64, 66]. Therefore, securing a reliable means to fabricate high-quality SFMO thin films and possessing a large MR would be immensely helpful for practical device applications. Manako et al. [71] have reviewed the growth conditions of SFMO thin films, which could only be obtained in a narrow range of deposition temperature and oxygen partial pressure. They studied the magnetoresistance behavior (~5 and ~20% at 300 and 5 K, respectively) of SFMO thin films grown on SrTiO3 (111) and (001) substrates. Their observation showed the large MR effect in (111) oriented films as compared to (001). This might suggest less scattering of carriers at grain boundaries for the (001) oriented film than for polycrystalline samples, since a perfect crystal is expected to show no MR at the temperatures far below
Despite the large amount of research reports available for the advancement of magnetoresistance in SFMO thin films, the growth conditions vary from one reference to the next and the results are still controversial. The growth parameters including various substrates and growth temperature can directly affect the surface quality and properties. Furthermore, the fabrication of SFMO-based MTJs structure was rarely reported [61, 68, 88, 89]. There are few groups, including Bibes et al. [68], Asano et al. [88], and Fix et al. [61, 89], who have fabricated the magnetic tunnel junctions and reported the tunneling magnetoresistance (TMR) at very low temperature (5 K). However, it was noticed that the above studies mentioned the growth of MTJ on single crystalline SrTiO3 substrates. In particular, as it was observed that one of the most important considerations for fabrication of the multilayered structure for spintronic device applications is surface/interface quality of the films. High vacuum conditions and
A pulsed laser deposition technique was used to deposit the SFMO thin films on SrTiO3 buffered Si (100) substrates. The experimental details are provided below.
\nTo optimize the growth conditions of SFMO thin films, substrate temperatures were varied from 500 to 800°C under the base vacuum pressure higher than (>5 × 10−6 Torr).
Prior to the deposition of SFMO films, STO buffer layer was grown on Si substrate in the presence of high purity (99.99%) oxygen at an ambient pressure of ~50 mTorr at 500°C and subsequently annealed
After the deposition of STO buffer layer on Si (100) substrate, SFMO thin layers were deposited at fixed temperature under the base vacuum pressure (~ 5 × 10−6 Torr) and subsequently annealed
The temperature of substrate was ramped down at 10°C per minute after deposition to prevent thermal shock and cracking of the film.
In this study, we fixed all growth parameters and varied only substrate temperature to optimize the growth conditions.
Figure 4, shows the XRD patterns of SFMO thin films grown on STO buffered Si (100) substrate at four different elevated temperatures ranging from 500 to 800°C at a step of 100°C. The growth of SFMO thin film at low temperature (≤ 600°C) do not produce the stoichiometric SFMO phase and an additional spurious peak of strontium molybdate (SrMoO4) phase (as indicated by * in Figure 1) was formed. The secondary impurity phases can be commonly observed at low growth temperatures of SFMO thin films [90, 91]. These impurity phases also develop the antisite disorder in the double perovskite lattice, which further affect the magnetic and magnetotransport properties of SFMO thin films [92, 93]. However, the aforesaid impurity phases were completely disappeared, and a single phase formation of polycrystalline SFMO thin films was observed when the growth temperature was raised to 700°C and above.
\nX-ray diffraction pattern of SFMO thin films grown on STO buffered Si (100) substrate at temperatures 500, 600, 700 and 800°C.
The SFMO thin film grown at 800°C shows a series of peaks at 2
The magnetoresistance properties of SFMO thin films deposited at different temperatures were measured using four probe resistivity setups at magnetic field up to ±8 T. The magnetoresistnace values are 0.009, 0.017, 0.16, and 0.35% for films deposited at 500, 600, 700, and 800°C, respectively, at room temperature and magnetic field of ±8 T as shown in Figure 5.
\nMR behavior of all the SFMO thin films at room temperature.
It is clearly observed that the film grown at low temperature (≤600°C) exhibited the small MR effect and further it was found to be enhanced with growth temperature. This enhanced behavior of MR may be due to the improvement in the crystallinity of the intragranular nature and also by decrement in the antisite disorders effect. Improvement in structural and magnetic properties strongly supports the enhanced magnetoresistance at room temperature. The MR values are 0.35 and 12% at 300 and 5 K for polycrystalline SFMO film grown at 800°C, which shows almost linear type of behavior with magnetic field.
\nPulsed laser deposition was used to fabricate SFMO/SrTiO3/SFMO Magnetic tunnel junctions on SrTiO3 buffered Si (100) substrate. The details of the experiments are given below.
\nIn the first step, we have optimized the thickness of STO layer by controlling number of laser shots, which was estimated in separate experiments by an empirical relation of thickness and laser pulse counts.
The STO buffer layer was grown on Si (100) substrate in the presence of high purity (99.99%) oxygen at an ambient pressure of ~50 mTorr at 500°C and subsequently annealed
The STO buffer layer was characterized by X-ray diffraction technique and found to be crystalline in nature.
The SFMO bottom electrode has been deposited at 800°C in the base vacuum pressure higher than ~5 × 10−6 Torr and annealed
The crystal structure and phase purity of SFMO bottom layer was examined by X-ray diffraction and micro Raman microscopy, which shows single-phase formation of SFMO thin film without any impurity phases. The XPS analysis confirms that the Fe:Mo ratio is almost equal (~1:1.1) over the surface of SFMO thin films.
Then ~2 nm STO barrier layer was deposited at 800°C with 5 × 10−6 Torr pressure. Again SFMO top layer was deposited at the same conditions as that of bottom SFMO electrode.
The shadow mask having lateral dimension 40 μm × 40 μm was used during the fabrication of trilayer SFMO/STO/SFMO structure.
Figure 6(a) shows the schematic of SFMO/STO/SFMO MTJ structure as pattern on STO buffered Si (100) substrate.
\n(a) Schematic of the typical SFMO/STO/SFMO MTJ structure. (b) Cross-sectional FESEM image of SFMO/STO/SFMO structure grown on STO buffered Si substrate at 800°C.
The thickness of the SFMO electrodes was kept (≥50 nm) to insure the half-metallic nature of grown SFMO layers [64] and the thickness of STO barrier layer was kept ~2 nm to examine the tunneling effect. The cross-sectional high-resolution field emission scanning electron microscopy (FESEM) image of SFMO/STO/SFMO MTJ structure grown on STO buffered Si (100) substrate as shows in Figure 6(b). The thickness of STO buffer layer is (~10 nm), which is grown on Si substrate. The presence of (~2 nm) STO barrier layer is clearly seen by bright horizontal contrast in the FESEM image, which is perfectly sandwiched between top and bottom SFMO electrodes.
\nFigure 7(a) represents the current voltage (I-V) behavior of SFMO bottom layer and SFMO/STO/SFMO MTJ structure at room temperature. It can be seen from Figure 7(a) that I-V behavior of the SFMO bottom layer is almost linear while it shows nonlinear and asymmetric behavior for SFMO/STO/SFMO MTJ at room temperature. This behavior of MTJ is quite different from the SFMO electrode layer and shows the typical characteristic of tunneling assisted transport across a thin insulating barrier [68, 94]. The tunneling conductance in SFMO/STO/SFMO magnetic tunnel junction is further confirmed by fitting the conductance (dI/dV) characteristics as shown in Figure 7(b) using Brinkman’s formula [95] intended for direct tunneling transport through a rectangular barrier. The thickness of the STO barrier layer was calculated from the fitting of the experimental data and was found to be ~1.5 nm, which is in close agreement with the estimated thickness of the STO barrier layer through FESEM image (~2 nm).
\n(a) Current-voltage characteristics of SFMO thin films and SFMO/STO/SFMO MTJ at 300 K. (b) Conductance fitting of SFMO/STO/SFMO MTJ.
Figure 8 shows the magnetic field dependence of the resistance for a SFMO/STO/SFMO MTJ with a junction area of 40 × 40 μm2, measured at 300 K. This is defined as (
Magnetoresistance behavior of SFMO/STO/SFMO MTJ at 300 K.
This type of abrupt change in resistance is related to the reversal of two electrodes within the constriction as already observed in Co/I/SFMO [68] and LSMO/STO/LSMO [96] MTJ trilayer junctions fabricated using similar approach. The TMR ratio of MTJ device is related to the spin polarizations
We had synthesized and presented the double perovskite Sr2FeMoO6 bulk compound, which shows the remarkable 11% low-field magnetoresistance at room temperature at low magnetic field (0.72 T). This value is one of the high MR value at 300 K, which was only achieved due to the optimized synthesis conditions to achieve better intergranular tunneling through grain boundaries. The brief overview of the synthesis of SFMO thin films has been provided. Furthermore, low deposition temperature and silicon substrate are required for their applications in microelectronics industry; therefore, we have fabricated the SFMO thin films on Si substrate at optimized growth conditions by pulsed laser deposition. The polycrystalline Sr2FeMoO6 thin films have been grown on STO buffered Si (100) substrates. We also made an attempt to observe the room temperature magnetoresistance in Sr2FeMoO6-based magnetic tunnel junctions (MTJ). The micrometer-sized (Sr2FeMoO6/SrTiO3/Sr2FeMoO6) devices were grown by pulsed laser deposition. FESEM micrograph analysis revealed the presence of ultrathin (~2 nm) STO barrier layer. Magnetization measurements showed the good ferromagnetic loop behavior with high Curie temperature (
Authors acknowledge the financial support from Council of Scientific and Industrial Research Grant at National Physical Laboratory, India and DOD Grant AFOSR-FA9550-16-1-0295 at UPR, Puerto Rico.
\nOrganizations are rapidly deploying Virtual Assistants aka bot technology [1] for automating communication, customer service, conversational commerce, product recommendation, education support, financial services, medical services, entertainment, social outreach and self-service tasks. They offer 24/7 service and fulfill the need of millennials [2] for real time responses. Virtual assistants enable organizations to reduce costs, increase brand loyalty and better serve customers. However, virtual assistants are built by humans using artificial intelligence (AI) technologies and have wide ranging ethical implications which are important for organizations and consumers to understand.
\nMany countries have published AI policy guidelines [3, 4]. These guidelines provide a broad level objective for the use of AI – to ensure human-centric, safe, and trustworthy AI. One of the most important aspect in all guidelines is ethics, “AI should be ethical, ensuring adherence to ethical principles and values”. Although, AI by its very nature is a form of statistical discrimination (finding patters in data), the discrimination becomes objectional when it places certain privileged groups at a systematic advantage and certain unprivileged groups at a systematic disadvantage. For example, the loan application algorithm gives higher credit scores to older males due to training bias. Objectional discriminations can arise due to multiple reasons like wrongly defining the business objective [5] of machine learning model, using unrepresentative data or data with existing prejudice [6] for training or by selecting wrong attributes or features of the AI model.
\nSignificant work has been done in the area of Ethically aligned design for Autonomous and Intelligent systems by IEEE [7]; and in the area of Facial recognition technologies [8]; but the area of virtual assistants has seen limited guidelines or regulation. California “Bot Bill [9]” provides only limited protection for consumers in terms of bot self-declaration.
\nIn subsequent sections of this chapter, we first define “What is ethics?” and then discuss on ethical principles for virtual assistants. These principles provide key ethical considerations that designers and consumers should understand. Next we discuss different types of virtual assistants deployed today deep, dive into the technology and learning techniques that make them ethical. Further, we analyze the guidelines and legislations that companies and governments have published. In the last section, we look into what is the probable future of super intelligent virtual assistants.
\nWhat is ethics?
\nAs per the Oxford dictionary ethics means “the moral principles that govern a person’s behaviour or the conducting of an activity”.
\nEthics is based on well-founded standards of right and wrong that prescribe what humans ought to do, usually in terms of rights, obligations, benefits to society, fairness, or specific virtues [10].
\nAn ethical virtual assistant should be designed with the ethical standards of the society it affects. These standards should extend to virtual assistants and their creators who should design, build and maintain virtual assistants to ensure that their interactions with consumers foster honesty, loyalty, refrain from doing harm, or fraud and provide the right to privacy.
\nIn the subsequent section, we discuss in detail the ethical principles of virtual assistants.
\nAI based virtual assistants ability to act intelligently has long been evaluated by the Turing Test [11] and Loebner Prize [12]. The focus is on intelligence of the system to respond to human questions. Looking through the lens of ethical principles, other questions arise, beyond “What can the virtual assistant answer?” For example, “Does the answer promote consumers interests or business interests like recommending the most profitable product which does not best suited”.
\nIn a recent paper published by Jobin, A., Ienca, M. & Vayena, E. on the global landscape of AI ethics guidelines [13], they found globally five emerging ethical principles that are deemed important – Transparency, Justice and fairness, Non-maleficence, Responsibly and Privacy. In this section we interpret these principles with a view on virtual assistants and what considerations should designers, developers and consumers understand when developing and interacting with virtual assistants.
\nAI transparency refers to the explainability [14], interpretability, disclosure [15] of the algorithmic models including their training data, accuracy, performance, bias and other metrics.
\nWhen dealing with virtual assistants, transparency [16] often refers to informing the consumers who they are chatting with ie virtual assistant, not actual human, sharing details on what information can the consumer search, and how his data will be used, stored, analyzed for improving experience.
\nBrands build trust with consumers by being transparent and honest in communication. Virtual assistants are an extension of brands consumer experience. If virtual assistants impersonate a human, it can lead to poor experience and lack of trust with the brand. This can also be harmful when interacting with consumers on sensitive areas like healthcare or banking.
\nDesigner and developers of virtual assistants should be transparent in disclosing information to consumers in terms of what they can search and disclose how their data would be shared and analyzed. When the consumers know what they can search they will be able to ask questions on the topics that virtual assistant has been trained on and get desirable answers. This will create a delightful experience. Further, consumer should have the choice to opt in their interaction data for other purposes like development of the AI models or for advertisements and more. This will help to gain consumer confidence in virtual assistants and increase adoption. Lastly, consumers should also have the option to connect to a real person, request callback or send an email if they are uncomfortable in interacting with a virtual assistant.
\nJustice means that AI algorithms are fair and do not discriminate against particular groups intentionally or unintentionally [17]. There have been numerous publications on fairness and how to identify, mitigate bias in Algorithms [18, 19, 20]. In case of virtual assistants justice, fairness and equity refer primarily to prioritizing the consumer interests and providing impartial recommendations [21] .
\nAI models on recommendation generally use techniques of collaborative filtering, ie filtering for consumer preferences based on information gathered from many similar consumers. The models constantly learn from consumer feedback ie likes or dislikes and adjust accordingly.
\nHowever these models can be biased based on the consumer training data or based on overarching business rules like recommend the most profitable product. For example, will the virtual assistant recommend the meat which is most expensive and near expiry date or the meat which is cheaper and fresh?
\nVirtual assistants being viewed favorable towards certain recommendations raises the question on fairness especially for consumers. When virtual assistants are used within an organization, then sometimes recommendation may rule driven, which is as per the employee policy.
\nDesigners and developers should regularly test the virtual assistants against the fairness metrics, publish them to consumers and also give consumers the option to provide feedback on recommendation. The more virtual assistant adapts to consumers interest and provides fair recommendation, the more popular the virtual assistant will become with consumers.
\nThis term is used to define consumers safety, security and the commitment that AI model will not cause harm for example, by spamming, hacking, discrimination, violation of privacy or abuse.
\nIn case of virtual assistants, we focus on abuse and sexual harassment for this principle. Abuse refers to both receiving abuse from consumers and giving back abuse to consumers.
\nMany times, virtual assistants are at the beginning of a consumers journey, and if the responses are not helpful it leads to frustration and abuse from consumers. Although, virtual assistants are AI models and do not have feelings (like humans), as consumers, we should refrain from abusing since it impacts the way we behave in society and transcends similar behavior towards even our fellow humans.
\nDesigners and developers need to design the conversation experience with consideration that virtual assistants will receive abuse. They should design the conversation flow empathically so that the consumers are provided a positive response and transferred to a more helpful channel like voice or email on request [22].
\nAnother consideration is gender stereo-typing ie the gender of virtual assistant. In many cases, virtual assistants have a default female voice or persona. Designers and developers can provide options to consumers to select the virtual assistant persona and alter language, voice, tone of responses specific to chosen persona.
\nIn a related study on sexual harassment of virtual assistants, “#MeToo: How Conversational Systems Respond to Sexual Harassment [23]” points different behaviors in commercial, supervised and unsupervised learning based virtual assistants. The unsupervised learning based assistants have more freedom in learning from user conversation and responding similarly. In these cases language correction models should also be deployed to protect users from chatbot abuse. For example, Microsoft’s Tay chatbot was corrupted in less than 24 hours by self-learning through user conversation [24].
\nResponsibility and accountability refer to the AI acting with integrity, clarifying the attribution of responsibility and data ownership. In case of virtual assistants this refers to being transparent, fair, disclosing information on responsibility, legal liability and data ownership to consumers.
\nThere has been much debate on who is ultimately responsible – is it the AI based virtual assistants or the humans who built it. Generally, terms of service agreement which consumers have to agree before using virtual assistants, define the limitations on responsibilities and liabilities in line with regulations.
\nData ownership requires special mention here. Questions typically arise on who owns the data when it is captured and generated during conversation with virtual assistant. For example, new data is generated when a virtual assistant interacts with consumers using voice. It will over time develop data related to consumers preferences (preference in music), personality [25] (words and tone of language), family (number of different voices in family or type of requests made ~ nursery rhymes) and more. Sometimes, organization may have built the business model on leveraging this derived data for profit. For example, Virtual assistant derives data on the age of your children and serves you advertisements on children toothbrush.
\nDesigner and developers should be transparent on data ownership and have an opt in feature, if the consumers want to share this new data generated or want to keep it private. If the business model of the virtual assistant is based on offering free services and leverage consumer data for advertisements, then that should also be transparent to the consumer.
\nPrivacy means that your personal information is kept confidential and only shared with consent. Many countries have passed laws and regulations to protect the privacy of their citizens like General Data Protection Regulation [26]. In relation to Virtual assistants, privacy is often referred in relation to data protection and security.
\nDeeper questions on privacy for Virtual assistants arise from
who has access to the conversation transcripts?
are the transcripts being used to profile the consumers
are the transcripts being shared with advertisers
are the consumer details anonymized before sharing?
are the transcripts being used for improving the AI model?
where are the transcripts stored
for how much time are the transcripts stored
can the consumer delete the transcripts?
is the communication channel encrypted
And so on.
\nDesigner and developers should be transparent on privacy policy and publish it online so that consumers can be informed on how their information is stored and protected. This will also help to develop trust in the virtual assistant and consumers will be more willing to share information if they know that they will be served better.
\nIn self-service technology, virtual assistants are on the higher maturity curve and are expected to understand and interact with consumers as “humans” to provide information or take action. If we look under the hood of virtual assistants, then we uncover three basic technology building bocks.
Channel of communication – Physical device (Amazon Echo, iPhone Siri), Messaging Platform (Slack, Facebook messenger), Website or App. The channel of communication generally includes voice interaction capability if available.
Conversational platform – Brain of the virtual assistant which has the rules and AI technology to understand consumer information and context.
Backend Database or Automation/APIs – This is the backend system from where information is retrieved or a specific task is executed. For example calling an API to retrieve weather information for location or setting up an alarm.
In this section, focus will be on the conversational platform which has to be designed with ethical consideration. There are many types of technologies deployed for virtual assistants ranging from simple click based predefined options, to pattern matching, natural language understanding and natural language generation. In the section below, different types conversational platforms are discussed with a view on ethical considerations.
\nMost commercial virtual assistants use pattern matching and natural language understanding AI models. The primary task of the AI model in this case is to classify intent of the question for pre-defined set of answers. The assistants can also understand specific details in the text like country name or time and more. For example if asked “What is the weather in Singapore?” assistant will classify this as the request to find weather information and also extract Singapore country name. This information will be passed to backend API to retrieve the temperature and presented back as the answer. Example of these virtual assistants used by business are IBM Watson Assistant, Microsoft Bot framework, Amazon Lex, Google Dialog flow and more. Learning on these platform is generally supervised and the knowledge corpus is limited to the business use case. Sometimes, extension of these platforms is done where a large document corpus is ingested and most relevant document is brought forward to the user based on search and retrieval techniques.
\nIn these platforms, it is the role of conversation designer and developer to ensure that the virtual assistant adheres with the ethical principles of Transparency, Justice & fairness, Non-maleficence, Responsibly and Privacy. Further, it is a good practice that document corpus is screened before being ingested into these virtual assistants to ensure relevant and proper responses.
\nSiri, Alexa and Hey Google are examples of mass market, virtual assistants. These virtual assistants are pre-trained from a large language corpus and have the ability to retrieve personal information from calendar, phonebook, music, credit card and more. The organization developing these Virtual assistants publish their terms of service, privacy policy [27] publicly and it is consumers decision to understand and then interact with them.
\nThe ubiquitous nature of these Virtual assistants poses a bigger question to society on how they should respond to different types of talk ranging from Rude talk, Abusive talk, Romantic talk or Suicidal talk. We discuss below two cases in detail, rude and romantic talk.
\nRude Talk – the virtual assistants tend to respond back positively with information without prompts for polite or rude requests. This has an influence on manners especially in case of younger consumers [28]. For example “Alexa can you please tell me the weather forecast for today” or “Alexa weather forecast today” – the answer would be the same. These assistants should try to add nicer words like “Thank you” when consumers say “please”.
\nRomantic Talk and Gender – when asked on gender, the virtual assistants tend respond on gender neutrality. However, by default they respond in a female voice. In the article by Jessi Hempel [29] in Wired she explains that people tend to perceive female voices as helping us to solve our problems. This also opens the door to romantic talk [30] for female persona based virtual assistants. Most of assistant are trained to handle this conversations by evading, or positively responding to consumers, but they rarely respond negatively [31]. This does extend in some cases to general acceptance of sexual harassment for assistants.
\nA special mention here to Virtual assistants who can talk about anything in the open domain. These assistants are trained using sophisticated deep learning AI models (un-supervised learning), have billions of parameters and are closest to how a human would sensibly and specifically answer questions. Many gigabytes of training data (dialog response) is ingested in these AI models and it generates the answers (natural language generation) based on learning. Example of these assistants are
Meena [32] - trained on 341 GB of text, filtered from public domain social media conversations.
DialoGPT [33] - large pre-trained
Mitsuku [34] – although this virtual assistant uses pattern matching technique, it has won many competitions.
Cleverbot [35] - this searches through its saved conversations, and responds to the input by finding how a human responded to that input when it was asked, in part or in full
Other than Mitsuku which uses supervised learning, for other virtual assistants, it is difficult predict responses since they are learning from dialog-response corpus. In these cases, it would be beneficial to have a language filter that checks for ethical considerations like abuse words and more before presenting the answers to consumers.
\nMany countries have published AI policy guidelines. These guidelines provide a broad level objective for the use of AI – to ensure human-centric, safe, and trustworthy AI. Most guidelines make the organization using AI responsible and accountable for their decisions and ask for the same ethical standards in AI-driven decisions as in human-driven decisions.
\nGeneral key points achieved from global guidelines are:
it should be lawful, complying with all applicable laws and regulations;
it should be ethical, ensuring adherence to ethical principles and values; and
it should be robust, both from a technical and social perspective since, even with good intentions, AI systems can cause unintentional harm.
Specially for virtual assistants, as defined above, five emerging ethical principles that are deemed important – Transparency, Justice & fairness, Non-maleficence, Responsibly and Privacy.
\nLegislation has been passed in California [36] to ensure Transparency of Virtual assistants. This law makes it mandatory for Virtual assistants (Bots) to disclose that they are not a real person and are virtual. Many other countries are passing laws and issuing guidelines to make it mandatory for Designers and developers to develop ethical Virtual assistants.
\nMany commercial organization have also issued their ethical guidelines. IBM has established an Ethics Board [37] which provides governance, review and decision making processes for IBM on ethics policies, practices, communication, research, products and services. They have also published open source toolkits which designers and developers can use to test whether there machine learning, AI models are transparent, fair and explainable.
\nGoogle Deepmind [38] has established a focus group which focuses on ethical standards and safety. They look it from the lens of Privacy, transparency and fairness; AI morality and values; Governance and accountability; AI & worlds complex challenges, Misuse and unintended consequences; Economic impact and inclusion.
\nMicrosoft [39] has issued guidelines for responsible bots, which are aimed at helping designers and developers to design a bot that builds trust in the company and service that the bot represents.
\nMany other companies have issued guidelines to ensure that the Virtual assistants developed on their platform maintain high ethical standards [40] like use supervised learning, divert issues on serios topics, do not spam users, keep user privacy, no advertisements and so on.
\nVirtual Assistant AI technology is growing at exponential pace. In the next few years we will have virtual assistants that surpass an average humans ability to respond sensibly and specially to a consumers question. Nick Bostrom [41] presents an interesting perspective on super intelligent moral thinking. In the distant future, as AI capabilities surpasses human intelligence, it could do better than human thinkers and have the correct answers on ethics by weighing up evidence. We have already started seeing the initial versions of these intelligent machines.
\nIBM Debater [42] is an example of super intelligent system. This AI system can independently debate a human and provide persuasive arguments on complex topics. The system is able to listen and understand a long spontaneous speech, model human dilemmas to form an argument and generate a whole speech of an opinion and deliver is persuasively. The system has participated live and won many debate competitions.
\nAnother example is from Soul Machines [43]. It provides Digital people ie animation of life like people on the screen. These screen animations of people is similar to an actual human who speak with expressions (eye, lips and facial movements). This provides a comfort feeling when interreacting with virtual assistant.
\nAs virtual assistants become a part of our daily life, ethical issues regarding virtual assistants will continue to grow. It is important for the society at large to discuss and agree on the ethical principles of Transparency, Justice & fairness, Non-maleficence, Responsibly and Privacy for virtual assistants.
\nThe views expressed in this chapter are my own and are not representative of my employer.
I thank Ali Soofastaei, who has been my mentor and guide for the initiative of publishing this chapter on Virtual Assistants and Ethical Considerations.
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\\n\\nMetadata for all publications is also automatically deposited in IntechOpen's OAI repository, making them available through the Open Access Infrastructure for Research in Europe's (OpenAIRE) search interface further establishing our compliance.
\\n\\nIn other words, publishing with IntechOpen guarantees compliance.
\\n\\nRead more about Open Access in Horizon 2020 here.
\\n\\nWhich scientific publication to choose?
\\n\\nWhen choosing a publication, Horizon 2020 grant recipients are encouraged to provide open access to various types of scientific publications including monographs, edited books and conference proceedings.
\\n\\nIntechOpen publishes all of the aforementioned formats in compliance with the requirements and criteria established by the European Commission for the Horizon 2020 Program.
\\n\\nAuthors requiring additional information are welcome to send their inquiries to funders@intechopen.com
\\n"}]'},components:[{type:"htmlEditorComponent",content:'Publishing with IntechOpen means that your scientific publications already meet these basic requirements. It also means that through our utilization of open licensing, our publications are also able to be copied, shared, searched, linked, crawled, and mined for text and data, optimizing our authors' compliance as suggested by the European Commission.
\n\nMetadata for all publications is also automatically deposited in IntechOpen's OAI repository, making them available through the Open Access Infrastructure for Research in Europe's (OpenAIRE) search interface further establishing our compliance.
\n\nIn other words, publishing with IntechOpen guarantees compliance.
\n\nRead more about Open Access in Horizon 2020 here.
\n\nWhich scientific publication to choose?
\n\nWhen choosing a publication, Horizon 2020 grant recipients are encouraged to provide open access to various types of scientific publications including monographs, edited books and conference proceedings.
\n\nIntechOpen publishes all of the aforementioned formats in compliance with the requirements and criteria established by the European Commission for the Horizon 2020 Program.
\n\nAuthors requiring additional information are welcome to send their inquiries to funders@intechopen.com
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