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",isbn:"978-1-83968-594-1",printIsbn:"978-1-83968-593-4",pdfIsbn:"978-1-83968-595-8",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"89d795987f1747a76eee532700d2093d",bookSignature:"Dr. Mahmood-Ur- Rahman",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9670.jpg",keywords:"Wheat Improvement, Food Security, Wheat Breeding, Wheat Genetics, Wheat Biotechnology, Wheat OMICS, Biotic Stress, Insect Resistance, Abiotic Stress, Climate Change, Genome Editing, Computational Biology",numberOfDownloads:86,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 26th 2020",dateEndSecondStepPublish:"September 23rd 2020",dateEndThirdStepPublish:"November 22nd 2020",dateEndFourthStepPublish:"February 10th 2021",dateEndFifthStepPublish:"April 11th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Ansari has published over 50 papers in international peer-reviewed journals in the field of molecular biology, biotechnology, and bioinformatics. He is a member of various international professional societies and a founding member of the Pakistan Society for Computational Biology.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"185476",title:"Dr.",name:"Mahmood-Ur-",middleName:null,surname:"Rahman",slug:"mahmood-ur-rahman",fullName:"Mahmood-Ur- Rahman",profilePictureURL:"https://mts.intechopen.com/storage/users/185476/images/system/185476.jpg",biography:"Dr. Mahmood-ur-Rahman Ansari is an Assistant Professor of Molecular Biology at Department of Bioinformatics and Biotechnology, GC University – Faisalabad, Pakistan. He obtained his BSc (Hons) in Plant Breeding and Genetics from University of Agriculture, Faisalabad, Pakistan in 2003. He got MPhil and PhD in Molecular Biology from National Centre of Excellence in Molecular Biology, Lahore, Pakistan in 2006 and 2011 respectively. He has published over 50 papers in international peer-reviewed journals in the field of Molecular Biology, Biotechnology, and Bioinformatics. Moreover, he has published more than 10 book chapters and edited 3 books so far. His research group aims to understand the molecular mechanisms of stress tolerance in plants. He is member of various national and international professional societies. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"70238",title:"Artificial Intelligence Tools to Better Understand Seed Dormancy and Germination",doi:"10.5772/intechopen.90374",slug:"artificial-intelligence-tools-to-better-understand-seed-dormancy-and-germination",body:'\nThe importance of the seeds began with the dawn of agriculture, around 12,000 years ago, although seeds have been collected and eaten for many thousands of years before crop domestication (20,000–100,000 years). This domestication involved the selection of the desirable traits, as a high yield, appropriated seed size and good resistance/tolerance to biotic and abiotic stress, avoiding undesirable ones (mechanism of dispersion and seed latency).
\nThe knowledge about the storage, distribution, germination, sowing, and harvest of seeds improved for the following centuries. The first written references on the germination of seeds can be found in religious texts or in the “naturalist-texts of Greeks and Romans.” In those documents, Theophrastus and Pliny, the elder explain various germination concerns, as the need of drying the seeds for storage or soaking them in water or in milk to stimulate their germination [1].
\nGermination is a complex physiologic process, beginning with water imbibition by the seeds and ending with the emergence of one part of the embryonic organ, the radicle. Harvested mature seeds are usually quiescent, meaning that they may survive many years with a standstill metabolism and low water content (<15%). Quiescent seeds must be imbibed for being able to activate its metabolism and germinate under suitable environmental conditions [2]. During seed imbibition, water uptake triggers the resumption of seed normal metabolic levels, and promote the damage repair occurred during drying. Once the seeds return to their normal metabolic state, an expansion of embryonic cells causes the embryo emergence and marks the end of germination. However, some imbibed and metabolically active seeds cannot germinate under a wide range of normal environmental factors and hence, they are considered dormant [3].
\nSeed dormancy plays a key role in the regulation of germination [4, 5]. Dormancy induction, maintenance, and release are determined by physiological and morphological seed characteristics and their control are governed by many genetic and environmental factors. Dormancy is then, a very complex biological process that involves multiple interactive factors (physiological, mechanical, and environmental), making it difficult to fully understand its performance despite the large number of publications available. Therefore, understanding how dormancy can be controlled and/or released should ensure the success of germination in desirable species with very interesting consequences in the socio-economic and research fields [4].
\nThe factors that affect dormancy release and germination are generally studied independently, although they are obviously interconnected: a) no germination is being possible without dormancy-breaking, b) it is almost impossible to define the beginning and the end of each process and, c) many factors may interact or counteract in both processes.
\nTraditionally, data from dormancy or germination studies were analyzed using traditional statistical methods, nevertheless, complex biological process such as germination and seed dormancy, cannot be fully understand by simple comparison of means among treatments, analysis of variance, regression models or simple algorithms, with those approaches being necessary to integrate multidimensional data to describe complex biological interactions [6, 7].
\nArtificial intelligence (AI) tools have been shown as useful techniques for establishing relationships between multiple variables (factors and parameters) [8, 9, 10]. In addition, several studies have shown the effectiveness of those AI tools, such as artificial neural networks (ANNs) combined with fuzzy logic or genetic algorithms for modeling and optimizing complex biological processes [11, 12].
\nIn this chapter, we describe how AI models can be used for a better understanding and selection of the critical factors that stimulate the physiological mechanism of dormancy-breaking and germination in seeds.
\nDormancy is an evolutionary characteristic that has increased the survival of plant species, through the inhibition of seed germination in adverse conditions [13]. Seed dormancy could be considered a germination absence under suitable environmental conditions, in an intact viable seed. This germination lack has evolved differently across the species and hence, several dormancy mechanisms have been developed according to the diversity of climates and habitats [5].
\nNikolaeva developed the first dormancy classification scheme including just two kinds of dormancy: endogenous and exogenous [14]. In the first one, embryo prevents germination, while in the second one, some seed structures or chemicals are responsible for germination inhibition [14]. Later, Bewley and collaborators described the mechanism involved in these two dormancies [2, 15]. According to these authors, the endogenous dormancy, re-named as embryo dormancy, can be induced by undifferentiated embryos, immature embryos, chemical inhibitors (present in seeds) and physiological constraints. On the other side, exogenous dormancy, re-named by these authors as coat-imposed dormancy, is caused by several covering tissues that interfered with or suppress seed germination. These tissues inhibit water uptake, gas exchange, chemical inhibitors release or cause mechanical restraint. Plants can exhibit one or both types of dormancy, acting simultaneously or successively.
\nThese different terms and definitions of seed dormancy have caused confusion within the scientific community because they include both morphological and physiological properties of the seeds. In the earliest years of this century, Baskin and Baskin [3] proposed a comprehensive classification, which accurately reflects all the points of view mentioned above, which comprise five main classes of seed dormancy: physiological (PD), morphological (MD), morphophysiological (MPD), physical (PY) and combinational (PY + PD).
\nBriefly, in PD class, seeds are water-permeable but present a physiological mechanism in the embryo that inhibits seed germination. Seeds with PD are affected by the phytohormone abscisic acid (ABA) and their physiological-inhibition grade (deep, intermediate and nondeep) varies according to their response to other phytohormone gibberellic acid (GA) and the breaking-dormancy requirements. Seeds showing MD dormancy have a small immature (underdeveloped, but differentiated) embryo and therefore, just needing an extra incubation time and suitable conditions for normal embryo development and following germination. However, in the case of seeds belonging to MPD dormancy, in addition to presenting an underdeveloped embryo, they also show PD that should be first broken to allow full embryo development, through warm/cold stratification and /or GA treatments. Concerning PY, it is caused by one or several cell layers that avoid the entry of water in the seed and can be broken under natural (high and/or fluctuating temperatures, fire, drying, digestive animal tract transit, and so on) or artificial (chemical or mechanical scarification or abrasion) conditions. By the formation of a gap between these waterproof coats, letting the water available for the embryo, germination can be restored. Finally, the combination of PY and PD, make the breaking the waterproof layers and the embryo physiological dormancy, necessary for achieving germination.
\nDespite all the above, it is important to emphasize that PD is the widest widespread, prevalent and abundant dormancy class for seeds from gymnosperms and angiosperms [3, 4, 5]. The wide distribution of plants with PD seeds have triggered in the appearance of different physiological mechanisms of induction or maintenance of dormancy. Moreover, these mechanisms are related to the environmental characteristics where the mother plant has grown [4, 5]. Many species with PD seeds show a cyclic change in dormancy states (primary and secondary), governed by several factors (Figure 1).
\nScheme of seed dormancy and germination control and regulation in response to environmental conditions. Several factors are involved on dormancy induction (red) and release (green arrows). Germination process can only be fully achieved if seed germination requirements (thresholds or sensitivity) overlap with adequate environmental conditions (green dotted area).
Induction of primary dormancy may impose during seed development by endogenous factors and its function is to prevent precocious germination, while seeds are being developed in the mother plant or immediately after their dispersal [2]. The plant hormone ABA is the main endogenous factor involved in the primary dormancy induction; however, exogenous factors such as environmental factors have also a high influence on this induction. In this sense, under optimal or at least favorable conditions, the seeds do not suffer any germination block, moving to a nondormant state (Figure 1). Under favorable environmental conditions, germination starts with water uptake, followed by embryo expansion (embryo leave the dormant state, mobilizes stored nutrients, full elongate) and finish when breaks the covering coats and radicle protrusion occurs [4, 5, 16]. In any other circumstance, a blockage will happen at any time and the seeds will return to the dormant state.
\nDormancy-breaking factors promote changes in dormant seeds (they cannot germinate in any condition) increasing their sensibility and allowing their germination under adequate environmental conditions [4, 5]. In addition, between dormancy and nondormant status, seeds are in a transitional state called conditional dormancy (CD), in which seeds are able to germinate but only in a small narrow of environmental conditions (Figure 1). In some species, under unfavorable conditions for germination, nondormant seeds may enter in a secondary dormancy status before germination [4, 17]. These seeds may continue in the transition between nondormant and dormant stage, which drives to a seasonal dormancy cycling (Figure 1). The cycle may continue for several years and is related to the maintenance of the soil seed bank, essential for the survival of plants communities.
\nThe presence of nondormant and dormant seeds in a population depends on the effect of several induction factors during their development. These factors represent the main checkpoints in the control of germination and are summarized below.
\nSeeds dormancy induction is highly correlated to the ABA content. This induction begins during seed development in the mother plant [18, 19]. In the initial stage, mother plant supplies ABA to seeds to prevent the premature germination [20, 21, 22]. In fact, Arabidopsis and maize mutants with reduced input of maternal ABA conduce to seeds germination in the mother plant [23, 24]. During the development, seeds begin to produce ABA by themselves and several scientific evidences demonstrated that this is the main factor required for inducing primary dormancy [5, 25, 26]. In fact, enhanced dormancy is evident in Arabidopsis mutants with overexpression of ABA biosynthesis genes, while ABA deficiency during seed development fails to induce primary dormancy. Therefore, it is widely accepted that the ABA synthesized in the embryo and endosperm is the most critical factor inducing seed dormancy [16, 26].
\nABA has also been proposed as the main factor involved in the dormancy maintenance of seeds, which still are dormant after their dispersion. In fact, de novo ABA biosynthesis has been associated to the maintenance of dormant state in several species (Hordeum vulgare, Helianthus annuus, and Nicotiana plumbaginifolia) [27, 28, 29]. In addition, Arabidopsis thaliana ecotype Cape Verde Island, imbibed seeds presented high ABA content and strong dormancy [17].
\nRecently, another phytohormone, the auxin indolacetic acid (IAA), has been found to regulate seed dormancy. Auxins have a similar effect than ABA, and transgenic seeds that overproduce auxin show a strong seed dormancy compared with wild-type seeds [30]. However, while the dormancy control of ABA by environmental signals is well-studied, the control of auxins in dormancy is not well-known yet [31].
\nThe environment has a strong influence on the induction of dormancy during seed development (Figure 1). The main environmental factors affecting ABA content are temperature and light. They promote genetic expression changes in the mother plant during the seed development, which modify the ABA concentration and seed sensitivity to this phytohormone [2, 19]. Generally, low temperatures increase the dormancy induction during seed development in the mother plant, by increasing the expression of genes related to ABA biosynthesis, while high temperatures rise expression of ABA catabolism expression [2].
\nDormancy induction is also influenced by natural light quantity (daily distribution), and quality (spectrum). Light daily distribution is controlled by the photoperiodic cycle: long days promote dormant seeds, while short days the opposite; the light quality is regulated via plant phytochrome (Prf): red wavelengths light (as white fluorescent) promote dormancy release, whereas far-red wavelengths (incandescent lights) promote dormancy induction and maintenance [32].
\nThere are other factors, such as soil characteristics, that may also affect dormancy induction. Mineral nutrients such as nitrates, phosphate, sodium, potassium, zinc, iron, cooper taken up by the mother plant and translocated to the seeds has been included as dormancy inductors [2].
\nFinally, other mother plant physiological characteristics such as age, seed maturation timing, and seed position, which also can alter the dormancy induction [18].
\nThe effect of ABA on dormancy induction and maintenance is counteracted by gibberellic acid (GA) since dormancy release depends mostly on ABA:GA balance (biosynthesis and catabolism) in seeds [33, 34]. This effect was demonstrated using plant deficient mutants. As example, Arabidopsis GA-deficient mutants present a strong seed dormancy and need exogenous application of GA for dormancy breaking [35], whereas mutants for genes involved in the negative regulators of GA biosynthesis pathway decreased the seed dormancy [36, 37]. Therefore, the release of latency depends on the concentration and sensitivity of the seeds to both phytohormones. Once seeds are imbibed, an increase in sensitivity and concentration of GA is necessary for dormancy release and some signals should trigger it. Nitric oxide (NO) has been proposed as a release dormancy signal since is related to the decrease of ABA sensibility and the increase of GA biosynthesis pathway in seeds of many species [38, 39]. Therefore, the dormancy release is established by the concentration and sensibility of both phytohormones in seeds.
\nOnce seeds are released from the mother plant to the soil seed bank, they begin to behave as sensors which may detect environmental factors (signals) and change their dormancy status, affecting the expression of genes related to phytohormone metabolism [40]. The combination of environmental signals and phytohormonal metabolism provides a complex network that allows controlling the germination according to ecological opportunities [5, 26].
\nThe dormancy-breaking mechanisms have been modified by evolution processes, according to the environmental signals in which the species lives. This facilitated that plant species with PD were dispersed and adapted to different habitats. Soil temperature and moisture are the major factors that indicate the seasonal changes. Both factors trigger the main modifications in the depth of dormancy, by changing the seeds sensibility to other environmental factors such as, light or nitrate among others [41].
\nConcerning those main factors, two main dormancy patterns have been found in the field. For species autumn-germinating, the dormancy is release during summer under warm and dry conditions, while for spring-germinating species the pattern shows a release of dormancy during winter under cold and wet conditions [42]. The evolutionary usefulness of dormancy is that the seeds need going through adverse germination conditions as a requirement to be able to break it and germinate conditions become appropriate. As example of this, has been described for seeds autumn-germinating that extend periods of warm temperature and dryness allowed release primary dormancy. This dormancy-breaking process is termed as after-ripening, promoting a decrease in ABA concentration in seeds, an increase GA sensitivity and widespread the range of other environmental requirements for germination. Ecologically, this requirement prevents germination during the hottest period of summer, being necessary for breaking dormancy and allowing the following germination in autumn.
\nThe time required of after-ripening for release dormancy is highly genotype-dependent. Moreover, using different temperatures and moisture content the after-ripening may be accelerated [43]. In fact, the after-ripening improved the germination of three autumn-germinating species (Anthocercis littorea, Dioscorea hastifolia, and Z. fruticulosum) when the temperature and moist conditions were modified from their normal summer conditions [44].
\nAs described above, seeds spring-germinating need periods of cold temperatures under moist conditions. This process is usually known as cold stratification or chilling. Seed stratification, promote the expression of genes related to GA biosynthesis and also decline the activity of some GA catabolic genes [45]. The stratification is required for majority nontropical species, which are spring germinating. Ecologically, this requirement prevents germination during their unfavorable season (winter) and allows their germination during spring, where suitable environmental conditions for seedling growth are settled (Figure 1). However, some species have a long period of cold stratification as requirement for break dormancy. In this case, a combination of after-ripening and cold stratification can be required in order to release dormancy [3].
\nOnce the temperature and soil moisture have modified seed dormancy depth, seeds increase their sensibility to other breaking dormancy and germination factors. For example, light is considered an important environmental factor for releasing dormancy. Many species only break their dormancy by exposure to white light (i.e., sunlight), while other seeds only release dormancy by a change in photoperiod (i.e., length of the day).
\nOxygen or carbon dioxide (soil gases) incorporated into the pores soil or dissolved in soil solution may affect the dormancy of the seeds. The seed responses to soil gases are highly variable and are dependent of the other environmental factors [2].
\nAfter dormancy release, nondormant seeds increase their sensitivity to the environmental factors favoring germination. To that end, GA stimulates germination in nondormant seeds by induction of hydrolytic enzymes, which stimulate the embryo growth, mobilization of endosperm storage reserves and weakening of tissues that are recovering the embryo [13]. Other phytohormones, such as ethylene and brassinosteroids, seems to be involved in some extension and limited impact on dormancy and germination, by reducing the influence of ABA effects in seeds [26, 46]. Indeed, exogenous applications of phytohormones as GA, cytokinins, or ethylene promoted germination in some species [47, 48].
\nOnce phytohormones have induced seed sensibility, several environmental factors are involved on seed germination.
\nTemperature is a good seasonal indicator for seeds germination capacity and rate, although may induce secondary dormancy too. Usually, the temperature ranges for germinate are opposite to the ranges for release dormancy, as we described above. The range of temperature, in which seeds are able to germinate, falls into the next three categories: minimum, optimum and maximum. These ranges are related with the adaption of each species to their habitat and the favorable conditions for later seedling growth. As example, Carex sp. evidenced different temperature requirements for seed germination since they need a cold stratification for break dormancy but they are not able to germinate at low temperature. In fact, the best germination temperature was determined around 25°C [49]. Contrary, a study with 50 autumn-germinating species with after-ripening requirements, dormant or conditional dormant, demonstrated that they germinated only at low temperatures [50].
\nWater, particularly soil moisture, is an essential factor for seed germination. Water availability affects to the rate and speed of germination. The imbibition process, explained previously, allows the normal metabolic process resumption in nondormant seeds. In addition, it allows the radicle growth and elongation for break the seed coat [18].
\nLight is well known for stimulating germination in several species, since some nondormant hydrated seeds acquire high sensitivity to this factor after releasing their dormancy by after-ripening or chilling. The light requirement for germination prevents this process in unfavorable time or places for the seedling growth. Natural (fire) or cultivation (agricultural management) events caused soil disturbances, letting soil seed bank to be exposed to sunlight and favoring their germination [2].
\nNitrate, nitric oxide and nitrites may stimulate the germination of many species. The ecological significance is that the seedling requires large amounts of nitrogen for optimal development. However, other germination factor/s may change the seed responsiveness to nitrate and their interaction regulates the germination response [51].
\nIn conclusion, dormancy release and germination are sequential processes and it is too difficult to distinguish the end and the beginning of each one. It seems that seeds need opposite environmental conditions to germinate that those for release dormancy (i.e., temperature as explained above). In addition, the environmental requirements for germination are species-dependent. Therefore, the combinations of multiple factors (endogenous and environmental conditions) regulating seed dormancy and germination, makes so difficult to understand and predict the best germination conditions [2, 18, 37, 41].
\nSeed dormancy and germination are complex biological processes, as described previously. Understanding these processes and, subsequently predict and optimize them is a quite difficult task due to the high number of variables (factors) involved in dormancy and germination [7, 11]. In fact, using traditional methods, only a few number of factors can be studied simultaneously, so the fully understanding of dormancy and germination still remain a challenge.
\nOther limitation derived on the different kind of data generated by biological processes. Then the importance of each factor needs to be determined by diverse statistical analyses depending on data nature (binary, discrete, continuous, and so on). In addition, the traditional statistical analysis is quite limited to the possible interaction among factors and does not allow predicting their best factor conditions to optimize both processes. Therefore, for these kinds of processes, simple stepwise algorithms are useless and therefore, more complex analytical tools are required; such as multivariable approaches (networks) using computational models [52].
\nTo develop a model some crucial steps are needed to be followed [7]:
Identify clearly the whole procedure (all steps) before built model.
Define and select the variables (factors and parameters) to study in the model.
Create a database with accurate data and select the kind of model to be built.
Validate the model to assure not significant differences among observed (experimental) and predicted (by the model) data.
Ishikawa diagram (Figure 2) is a useful chart to identify the causes of a specific event in order to fix some factors as controlled variables and select some as independent variables to be test experimentally [52]. This diagram shows the high amount of factors and variables involved in the germination process and helps to identify some relationships among these factors overall process. After detecting the key variables (inputs) for germination, it is necessary to define the parameters for weight their effects (outputs) and an appropriate model. Although several models have been used to integrate data from complex biological processes, recent studies have shown the effectiveness of artificial intelligence tools as artificial neural networks (ANNs), genetic algorithms and neurofuzzy logic for modeling and optimize them [7, 12, 53, 54].
\nIshikawa diagram showing the large amount of factors (endogenous and exogenous) involved in seed dormancy (induction and breaking) and germination.
Artificial Intelligence (AI) tools are problem-solving algorithms used when the number of solutions of one or several problems is huge, since they are capable of dealing with complex data in a versatile and powerful way [10]. This technology has been applied successfully to biomedical, pharmaceutical, and chemical applications for commercial and industrial purposes [8, 10, 55]. In addition, AI tools were used in basic and applied science research including ecology, environmental sciences, agriculture, food science, plant biology and biotechnology [9, 56, 57]. In this section, we summarized the most basic and important characteristics of the AI tools employed in plant biology [7].
\nANNs are AI tools that allow discovering nonlinear relationships among factors (inputs) and parameters (outputs) data. ANNs are one of the most effective method for revealing links among variables particularly if database are large and it is difficult to find direct relationships. In the case of germination (Figure 2), links between all factors and their effects on dormancy induction, dormancy-breaking and germination seems to be very complex, and difficult to be found using traditional statistical methodologies, but appropriate for ANNs. However, this technology has some limitations, related with the difficulties for results interpretation. In order to avoid this problem, ANNs are usually combined with other AI tools such as genetic algorithms or fuzzy logic (described below), which make easier the result interpretation [55, 58].
\nGenetic algorithms (GA) are a heuristic algorithms based on genetic and natural selection. They are considered heuristic because generate useful solutions against problems. Concerning to plant biology, these algorithms have been used for optimization bioprocess [59]. Hybrids models between ANNs and GA have been performed allowing developing more accurate models for predict, optimize and control some biological process. Models achieved with this AI tools allowed determining the combinations of variables (factors or inputs) that provide the best results [7].
\nFuzzy logic tool is a computational tool, which allows analyzing and making deductions from uncertain or fuzzy data. Fuzzy logic assigns qualitative values using linguistic terms and degrees of membership called membership functions [7]. Interaction between linguistic terms (as low, medium or high) and membership functions allow the computer making meaning values to study [59]. Moreover, this tool explains the behavior of a complex system using an easy language and improves description about any complex task or process. Knowledge acquired after modeling with a fuzzy logic-based system, is expressed by IF-THEN rules. These rules explain antecedent conditions of inputs variables (factors studied) and their consequent effect on the output variables (parameters measured) [7]. In conclusion, fuzzy logic tools have the ability for find consistent patterns or relationship between factors of complex process and generate understandable knowledge in an explicitly format (linguistic rules) [60, 61].
\nFuzzy logic can be combined with ANNs forming neurofuzzy logic. This is a hybrid system that combines the adaptive learning capabilities from ANNs with the flexibility of representation of fuzzy logic [55].
\nAI is a novel technology in plant biology, and only a very scarce literature has published applying AI tools to germination and dormancy. However, these works are very interesting since they suggest that AI tools may predict and optimize germination and dormancy processes.
\nAs previously discussed, a representative study of seed germination requires large experimental designs to include the effect of multiple factors (Figure 2). Therefore, the experimental design implies many treatments, replicates and, a huge number of seeds. However, not always is available a suitable size sample of seeds to draw clear conclusions. Under those circumstances, AI tools are an excellent alternative to conventional statistical methods. Advantageously, neurofuzzy logic technology allows working with not well-defined design spaces (reduced number of treatments) and different kind of data at the same time [62, 63].
\nThe first papers published using AI in germination [64, 65], were devoted to predict field seed weed emergence, since is essential for minimizing economic losses and improve crop yield and management. Then, they really focus more in weed management and control than in elucidate the factors involved in seed weed germination. In those works, germination of the weed Avena fatua, was predicted with more accuracy with ANN models than with nonlinear regression analysis [64, 65]. In addition, those models were improved including some dormancy parameters such as after-ripening and implemented a genetic algorithm to optimize them. In this optimization process the mean square error between their experimental and training data were minimized. Therefore, they allow to obtain more parsimonious models and with better predictive capacity [66, 67].
\nMost germination research was carried out on seeds with commercial interest, however, many wild species, that never have been cultivated by humans, have deep dormancy and present seeds with underdeveloped embryos and with physiological dormancy. Moreover, some of those plants with poor germination are classified as vulnerable and endangered plants [68]. This is the case of Eryngium viviparum a threatened plant belonging to Apiaceae. This family has many species are well-known by a nonuniformity germination due to MD and MPD seeds. Recently [68], the hybrid neurofuzzy logic tool was used to decipher the relationship among several dormancy-breaking and germination factors (inputs) and several parameters (outputs), such as germination rate and embryo growth (E:S ratio). Neurofuzzy models allowed to found the most critical factors involved in the seed responses. In addition, IF-THEN rules pointed out the interaction of those factors to increase or promote the germination rate and the E:S ratio. The model revealed that the best germination rates were obtained with the combination of 1 mg L−1 GA3 (gibberellic acid) and high (24°C) incubation temperature and the combination of long incubation (20 weeks) and short warm (25°C) stratification periods (4 weeks) [68].
\nMore recently, also neurofuzzy logic was used successfully in order to discover the critical factors that break dormancy and increase the germination rate in several kiwifruit cultivars, then allow to describe the best conditions for kiwifruit seeds germination [69]. The next factors were investigated: a) the effect of stratification time and type on dormancy-breaking and the effect of thermo-photoperiod on germination. The results obtained demonstrated that neurofuzzy logic models greatly facilitate the data analysis and pointed out the critical role of cold-stratification time (long periods at 4°C) and stratification treatment (using gibberellic acid) on kiwifruit seed germination. In conclusion, neurofuzzy was able to model with high accuracy and predictability, to obtain a set of rules very useful for understand the cause-effect among the studied factors and dormancy-breaking and germination [68, 69].
\nSeed germination is a very complex bioprocess, dependent on many interacting factors. This kind of processes are not fully understood due to experimental limitations (low number of factors studied simultaneously or poorly designed experiments), which do not allow to study simultaneously all interactions among the factors involved. Currently, due to the emergence of computer-based technologies such as AI tools, those bottlenecks can be avoided. Artificial intelligence tools provide useful algorithms for studying complex processes, big datasets, being a quite novel technology in seed science.
\nIn recent papers, ANNs combined with fuzzy logic had allowed to predict the germination of weed species, in a much easier way than traditional methods as statistical regressions. In addition, the use of ANNs combined with genetic algorithms allows to build up computer models to optimize, with high accuracy, the germination of these weeds, and hence, decreasing the economic losses in crop production. Moreover, the hybrid AI tool, neurofuzzy, demonstrated to be a successful technology to decipher the most critical factors and their interactions for increased germination and reduced the dormancy impact in some species.
\nIn a near future, it seems that AI tools would be essential and very useful tools in germination studies, for the selection of most critical factors, with good accurateness in decipher the interaction between environment and physiological factors on dormancy and germination.
\nThis research was supported by TREMEDAL-Inland wetlands of Northern Iberian Peninsula: management and restoration of mires and wet environments European Union (LIFE11 NAT/ES/000707, 2012-2015). This work was also funded by Xunta de Galicia, Spain (CITACA Strategic Partnership, Reference: ED431E 2018/07) and “Red de Uso Sostenible de Recursos y Residuos” (ED431D 2017/18).
\nThe authors declare no conflict of interest.
Bismuth oxide is an important material with many promising applications [1, 2]. In particular, bismuth-doped optical fibers, as a promising active medium for amplifying and lasing in the 1.1–1.8 μm range [3, 4], have been extensively studied, ever since their broadband near-infrared (NIR) fluorescence properties were first reported by Fujimoto et al. [1]. Thereafter, an amplification at the 1300 nm band in Bi-doped silica glass was realized [2] and an optical amplifier and fiber laser were achieved [4, 5, 6]. Previous investigations have demonstrated that the valence state of Bi ions varies in glass materials [7, 8, 9, 10, 11]. However, the valence conversion mechanism of Bi-related materials for silica optical fibers remains unclear.
In addition, the effects of radiation on the fluorescence properties of Bi-doped glass or optical fibers have been previously studied [12, 13, 14, 15, 16, 17, 18]. In Refs. [16, 17, 18], the radiation-induced photoluminescence (PL) effect of Bi-doped silica optical fibers was investigated and the relationships between the radiation-induced optical properties and defect centers in Bi-doped silica fibers (BDFs) were reported. Moreover, the fluorescence intensity was enhanced by UV irradiation [11, 12]. Shen et al. [14] also reported fluorescence enhancement from exposing Bi-doped borosilicate glass to a radiation environment. The photo-bleaching effect on Bi-doped glass fiber with a 532-nm laser treatment was studied [15]. These results provide deeper insight into the nature and formation mechanism of PL [19]. Furthermore, the magneto-optical properties of the Bi-doped silica fibers were studied before and after irradiation, and radiation-induced magneto-optical phenomena were found [20, 21, 22]. Finally, thermal effects on the luminescence properties of Bi co-doped silica fibers were studied [23, 24, 25].
However, the nature of the NIR fluorescence properties in Bi-doped glass or silica optical fibers is still unclear. Although many studies have reported the luminescent properties of Bi co-doped fibers, there are few reports on the effect of irradiation on the optical properties of Bi-related co-doped silica optical fibers.
In this chapter, three kinds of Bi-related co-doped silica optical fibers, including Bi/Al, Bi/Pb, and Bi/Er co-doped fibers, are fabricated using atomic layer deposition (ALD) combined with a modified chemical vapor deposition (MCVD) process. The optical properties of bi-related materials co-doped silica optical fibers (BRDFs) that are influenced by irradiation are investigated, including luminescence, lifetime decay, magnetic-optical, and unsaturable absorption, and the changes in these optical properties are compared.
Currently, the fabrication technologies of different doped fibers such as rare earth-doped fibers mainly use a solution-doping chemical vapor deposition technique. However, the technology lacks uniformity and consistency, and doping materials are easily volatilized and form clusters in a high-temperature environment, which limits the excellent performance of the fabricated doped fibers. Recently, a novel doping method, ALD technology, has been developed. It is not only an advanced deposition technique [5, 26, 27, 28, 29, 30] that allows for ultrasmall dopants of a few nanometers to be deposited in a precisely controlled way but also a chemical vapor deposition technique based on the sequential use of self-terminating gas–solid reactions. In particular, the novel technology involves a self-limiting surface reaction, whose advantages include a low-temperature process, good uniformity, favorable dispersibility, high doping concentration, and wide range of materials used. To date, there have been only a few reports [30, 31, 32, 33, 34] regarding the preparation of rare earth optical fibers by ALD.
ALD technology typically involves a cycle of four steps that is repeated as many times as necessary to achieve the required doping concentrations. As an example, we perform ALD on Al2O3, using Al2(CH3)3 (Trimethylaluminum, TMA) and H2O as the reactants. The detailed deposition process is shown in Figure 1. Step 1: A pulse precursor vapor of TMA reacts with the inner surface of the substrate tube. With the optimized choice of precursors and reaction conditions, the reaction of this step is self-limiting. Step 2: Purging all residual precursors and reaction products. Step 3: Low damage by remote exposure of the surface to reactive oxygen radicals, where these radicals oxidize the inner surface and remove surface ligands. This reaction is also self-limiting because of the limited number of surface ligands. Step 4: Reaction products are purged from the chamber. Only Step 3 varies, between H2O and O2 plasma by thermal processing. As each cycle of the ALD process deposits a layer of sub-angstrom thickness, the atomic scale ranges of deposition can be controlled through the process.
Specific process of ALD technology deposition.
The reaction of X(thd)3 (X: metal ions, such as Bi, Pb, and Er; thd: 2,2,6,6-tetramethyl-3,5-heptanedionato) and H2O can be described by Eqs. (1)–(3) [35]. The whole reaction can be written as follows:
which involves two processes: process A in Eq. (2) is the hydroxyl on silicon reacting with the X source to obtain Si-O-X(thd)2; process B is obtaining Si-O-X(OH)2 by the reaction in Eq. (3) of H2O and Si-O-X(thd)2 with the termination of ▬OH groups. On repeating the ABAB (A and B represent different reaction processes, respectively) operations, an X-doped layer with the desired thickness is obtained. Similarly, Al2O3 can be deposited using these following analogous reactions.
The fabrication process of the BRDFs can be divided into four steps, as shown in Figure 2. First, a porous soot layer is deposited inside the silica substrate tube using the MCVD method. In this process, chemical reactions in the gas phase generate a fine soot of silica that coats the inner surface of the substrate tube, which is then sintered into a semi-clear soot layer. Second, Bi, Pb, or Er ions are introduced on the surface of the porous soot layer using the ALD technique (TFS-200, Beneq, Finland). This results in the formation of bismuth oxide, lead oxide, and erbium oxide with the precursors of bis (2, 2, 6, 6-tetra-methyl-3, 5-heptanedionato) bismuth (III) (Bi(thd)3), bis (2, 2, 6, 6-tetra-methyl-3, 5-heptanedionato) lead (III) (Pb(thd))3, and bis (2, 2, 6, 6-tetra-methyl-3, 5-heptanedionato) erbium (III) (Er(thd))3 (supplied by Shanghai J&K Scientific Ltd), respectively. They mainly react with water or ozone to form the metal oxidation layer, the O3 that originated from the O2. Third, germanium oxide is doped into the fiber preform core by the MCVD process, and then a Bi-related co-doped optical fiber preforms with a Ge-doped higher index core that is formed by collapsing on an MCVD lathe heated by a high-temperature oxyhydrogen flame. Finally, the preform is drawn into a doped optical fiber with a Bi-related material.
Fabrication process of the BRDFs based on ALD + MCVD technology.
For optical fiber material, a perfect structure is visualized as a co-doped ion random network of SiO4 tetrahedrons joined at the corner, and different ions are doped into irregular vitreous silica, forming a stable network structure [36]. It is important to accumulate further knowledge regarding the influence of radiation on optical fiber materials, including material network structures, defect centers, and optical properties. Radiation as an effective method can induce changes in the optical properties of materials. It mainly involves the process of high-energy particles interacting with fiber materials, including the photoelectric effect, the Compton effect, the electron pair effect, and more. For BRDFs, irradiation significantly improves their optical properties, which mainly accounts for the variation in the valence states of Bi (Bi5+, Bi2+, Bi+, Bi0, defect centers, Bi clusters, Bi2−2 dimers, or Bi atoms). Here, gamma rays are selected as the irradiation source, mainly due to their short wavelength and strong penetrating ability. The effects of gamma ray irradiation on the optical properties of BRDFs, including Bi/Al co-doped silica fibers (BADFs), Bi/Er co-doped silica fibers (BEDFs), and Bi/Pb co-doped silica fibers (BPDFs), are investigated.
The radiation-induced PL properties of BADFs were investigated in [19]. The PL spectra in the inset of Figure 3 reveal two emission bands at approximately ~1150 and ~ 1410 nm, corresponding to the aluminum-related Bi active center (BAC-Al) and the silicon-related Bi active center (BAC-Si), respectively. Figure 3 illustrates that the fluorescence intensities of BAC-Al increased by 0.73, 2.25, and 1.35 dB at 1150 nm with 1.0, 2.0, and 3.0 kGy of irradiation, respectively. The fluorescence intensities of BAC-Al in the BADF samples increased with the increase in radiation dose (0–2.0 kGy) and then decreased when the radiation dose exceeded 2 kGy. Moreover, the change in the fluorescence intensity of BAC-Si trended similar to that of BAC-Al; however, the fluorescence intensity of BAC-Si increased considerably more. Furthermore, the fluorescence intensity of BAC-Si was approximately four times stronger than that of the unirradiated fiber sample.
Fluorescence intensity of BAC-Al and BAC-Si as a function of radiation dose; inset are the PL spectra of the BADF samples before and after γ-ray irradiation.
For BEDF, five BEDF samples were irradiated with cumulative doses of approximately 0.3, 0.5, 0.8, 1.5, and 3.0 kGy at room temperature. The radiation dose rate was 800 Gy/h. Under excitation at 980 nm (pump power is 1.8 mW), the fluorescence spectra of BEDF samples were measured, as shown in Figure 4. For BAC-Al, as the radiation dose was increased, the fluorescence intensity first increased and then decreased. With a 0.3 kGy dose of irradiation, the fluorescence intensity of BAC-Al in the BEDF sample is slightly higher than that of the pristine fiber, as shown in Figure 4(a). However, when the radiation dose was less than 0.5 kGy, the fluorescence intensity of BAC-Al was significantly lower than that of the pristine fiber. In addition, the fluorescence intensity of BAC-Si in BEDF showed the same trend in Figure 4(c) (red curve). The fluorescence of Er ions at 1550 nm was also observed, as shown in Figure 4(b). For Er ions, the fluorescence intensity decreased with an increase in the radiation dose and fluorescence enhancement at low-dose radiation (<0.5 kGy) such as Bi ions did not appear..
Fluorescence spectra of BEDF samples at different bands with different radiation doses. (a) 1100, (b) 1550 nm, and (c) the variations of the fluorescence intensity at 1100, 1450, and 1550 nm.
For BPDF, five BPDF samples were irradiated with cumulative doses of approximately 0.3, 1.0, 1.5, 2.0, and 3.0 kGy at room temperature. The radiation dose rate was 800 Gy/h, which is the same as in the other experiment. The fluorescence spectra of BPDFs at different doses under 830 nm pumping are shown in Figure 5(a). Comparing the PL spectra before and after irradiation, the shape did not change significantly. The fluorescence spectra of the fiber samples range from 1100 to 1600 nm with a peak at 1420 nm, which is derived from BAC-Si. The change in the fluorescence peak of BAC-Si is shown in Figure 5(b). With an increase in the radiation dose, the fluorescence intensities of BAC-Si first increased and then decreased with a further increase in the radiation dose. Moreover, when the radiation dose was 1.5 kGy, the fluorescence intensity of BAC-Si was two times that of the unirradiated BPDF. That is to say, low-dose irradiation can promote the formation of BAC-Si, enhancing the fluorescence intensity. For radiation doses up to 3.0 kGy, the fluorescence intensity of BAC-Si was still higher than that of untreated fiber. This indicated that the BPDF samples had a certain degree of radiation resistance, which has great potential for photonic applications of optical fiber amplification devices in harsh radiation environments.
(a) PL spectra of BPDF samples with different radiation doses and (b) variation of the fluorescence intensity at 1420 nm.
The luminescence decay curves of the Bi-related active centers in BEDFs and BPDFs were measured using a fluorescence spectrophotometer (Edinburgh FLS-980, England) equipped with an nF900 flash lamp. The fluorescence lifetime decay curves of BAC-Al in BEDF samples before and after radiation are shown in Figure 6(a). In order to compare the fluorescence decay curves of the BEDF samples with different radiation doses, a single exponential function was used to fit them. The relationship between fluorescence lifetime and radiation dose is shown in Figure 6(b). When the radiation doses were 0, 0.3, 0.5, 0.8, 1.5, and 3 kGy, the fluorescence lifetimes of BAC-Al were 564, 599, 585, 560, 559, and 553 μs, respectively. These results demonstrated that their lifetimes increased at low radiation doses (0–0.3 kGy) that were increasing, whereas at higher radiation doses (0.5–3 kGy), their lifetimes were decreased.
(a) Luminescence decay curves with different radiation doses and (b) variation in the fluorescence lifetime.
For comparative analysis, the fluorescence lifetime of the Er3+ ions at 1534 nm was also measured, as shown in Figure 7(a); when the radiation doses were 0, 0.3, 0.5, 0.8, 1.5, and 3 kGy, the fluorescence lifetimes of the Er3+ ions were 11.26, 11.13, 11.11, 11.10, 10.73, and 10.23 ms, respectively. The fluorescence lifetimes of Er3+ ions decreased with increasing of radiation doses, as shown in Figure 7(b).
(a) Luminescence decay curves of Er3+ active center in BEDF with different radiation doses and (b) variation of the fluorescence lifetime.
For the BPDF samples, the luminescence decay curves of BAC-Al are presented in Figure 8(a). The single exponential function is a close fit. The luminescence lifetimes of BAC-Al were 740, 699, 573, and 500 μs for radiation doses of 0, 0.3, 1.0, and 3.0 kGy, respectively. Further, under the radiation conditions, the lifetimes of BAC-Al decreased rapidly, as shown in Figure 8(b). It is inferred that the radiation increases the probability of the non-radiative transition, which may be attributed to the faster process whereby the electron in the excited state returns to the ground state or to the role of lead ions. To confirm this hypothesis, a more detailed experiment is required in the future.
(a) Luminescence decay curves of BAC-Al in BPDF samples with different radiation doses and (b) variation of the fluorescence lifetime.
Unsaturable pump absorption (αus) is ideally determined by the direct measurement of the remaining absorption of pump light. The saturable pump absorption (αs), which is a measure of the effective pump absorption of the fiber used for the radiative emission, decreases with the increasing pump power. The pump absorption consists of αus and αs. In fact, we focus more on the merit Mα, defined as Mα = αs / (αs + αus), which represents the ratio of useful pump absorption, αs, to the total pump absorption at the pump wavelength. This fraction is a key indicator of useful pump absorption and has a direct correlation to laser efficiency. Here, the unsaturable absorption characteristics of BEDFs at 980 nm before and after irradiation were investigated, as shown in Figure 9(a). When the radiation doses were 0, 0.3, 0.5, 0.8, 1.5, and 3 kGy, the αus values of the BEDF were 40.6, 37.0, 40.7, 43.5, 46.8, and 49.6 dB/m, respectively. As the radiation dose increased, αus first decreased and then increased, as shown in Figure 9(b). According to the relationship between αμs and the radiation dose, the decrease of αμs in the sample at a low radiation dose (0.3 kGy) may be attributed to the local structural change of Bi ions. Moreover, when the radiation dose was below 3.0 kGy, the αs of the BEDF (3.6 dB/m) was smaller than that of the unirradiated BEDF. At the same time, their corresponding Mα values were also calculated as 58.6%, 57.5%, 54.8%, 53.3%, 52.2%, and 50.2%. Hence, the Mα of BEDF continuously decreased with an increase in the radiation dose.
(a) Unsaturable absorption characteristic of BEDF samples with different radiation doses and (b) variation of the αus and Mα.
The unsaturable absorption characteristics of the BPDF and Bi-doped silica fibers are shown in Figure 10. The unsaturable absorption of the Bi-doped silica fiber (αus1) and the Pb/Bi co-doped silica fiber (αus2) at 830 nm were approximately 18 and 8 dB/m, respectively, and their corresponding saturable absorptions were 72 dB/m (αs1) and 45 dB/m (αs2), respectively.
Unsaturable absorption characteristic of Pb/Bi co-doped fiber (black curve) and Bi-doped fiber (red curve) at 830 nm.
The derived merit Mα of the Pb/Bi co-doped silica fiber was approximately 85.1%, which was larger than that of the Bi-doped silica fiber (80.0%). A high merit Mα meant that a large proportion of the pump photons would participate in the excitation of the active ions, promoting the desirable luminescence process at the corresponding bands. As such, the larger the Mα value, the higher the laser efficiency. Compared with the fiber-doped Bi ions only, the Pb/Bi co-doped silica fiber exhibited improved unsaturable characteristics. This would be beneficial for fiber lasers and amplifiers.
After the BPDF samples were treated with different radiation doses, the unsaturable absorption characteristics were measured as shown in Figure 11(a), and both αus and αs changed significantly. With an increase in radiation dose, αus trended with a gradual increase, whereas αs decreased and exhibited a small fluctuation. Furthermore, Ma trended similar to αs, as shown in Figure 11(b). For the BPDF, the radiation effect on αus was small, similar to the effect of radiation on the fluorescence lifetime of the Er3+ ions.
(a) Unsaturable absorption characteristics of BPDF at 830 nm with different radiation doses and (b) unsaturated absorption coefficient and Mα value with the function of radiation dose.
To further study the influence of radiation on the characteristics of Bi ions, the effect of radiation on the magnetic-optical properties of the Bi-doped silica fiber (BDF) was investigated by comparing it with other silica fibers, such as SMF and Pb-doped silica fiber.
The Faraday rotation degree of the BDF in different magnetic fields ranging from 0 to 118 mT was measured. The slope of the Faraday rotation curve, marked as βi, where i = 1–7, in Figure 12(a), determined the Verdet constants of the corresponding fiber samples. The Faraday rotations of the fiber samples were proportional to the intensity of the applied magnetic field. The slope of the rotation angle of BDF (β2) before irradiation was larger than that of SMF (β1). After the irradiation, the trend of the slope of the rotation angle changed from β2 to β4 clockwise, and then from β5 to β7 anticlockwise. The Verdet constant (1.64 rad/(Tm)) of the BDF before irradiation is 26.0% larger than that of SMF (1.29 rad/(Tm)), and the Verdet constant value is positive, indicating that the BDF material has diamagnetic properties. After radiation, the Verdet constant of the SMF increased with increasing radiation doses, as shown in Figure 12(b); however, those of the BDF decreased at low radiation doses (<0.3 kGy). In particular, after 0.3 kGy of irradiation, the Verdet constant of the BDF became negative, showing that the BDF material has a paramagnetic property. Its Verdet constant value was positive and increased with the increase in radiation doses from 0.5 to 3 kGy. The Verdet constant of the BDF after 3.0 kGy of irradiation became 1.87 rad/(Tm), which is 23.84% larger than that of SMF with 1.51 rad/(Tm) and 44.96% larger than that of SMF without radiation.
Relationship between Faraday rotation and (a) magnetic field density and (b) radiation doses.
For the irradiated SMF and Pb-doped silica fibers, their Verdet constants always increased with an increase in the radiation dose, as shown by the red and black curves in Figure 13. With a further increase in radiation doses, the Verdet constant of the SMF became essentially constant, which may be due to the fact that the concentration of Ge-related defect centers induced by radiation tended to be saturated. For the Pb-doped silica fiber, the Verdet constant also increased with an increase in the radiation dose (0–1.5 kGy). The Verdet constant of the Pb-doped silica fiber was higher than that of the SMF. This result indicated that gamma-ray radiation enhanced the Verdet constants of the fiber samples, especially for Pb-doped silica fibers. Irradiation not only induced Ge- and Si-related defect centers such as Si′, Ge′ color centers, but also led to new Pb-related defect centers in the Pb-doped silica fibers. These defect centers increased the electron transition probability of Pb2+ in 1S0 → 1P1 and contributed further to the orbital electron spin. This may be why the increase of the Verdet constant for Pb-doped silica fiber is faster than that for the SMF with an increase in the radiation dose (1.5–2.5 kGy). Therefore, it is supposed that gamma rays improve the magneto-optical properties of fibers.
Verdet constants of Bi-doped silica fiber, Pb-doped silica fiber, and SMF with different radiation doses.
For the BDF irradiation, with the increase in the radiation dose, the Verdet constant of the BDF decreased first and then increased. In particular, under 0.3 kGy, the Verdet constant had a negative value, as shown by the blue curve in Figure 11. The change in the Verdet constant may mainly result from Bi ions, which present the formation of multiple valence states in the fiber, such as Bi0, Bi1+, Bi2+, Bi3+, and Bi5+. Furthermore, among various valence states, the conversion may be possible under radiation treatment. These different valence states have different outer electronic shell structures. Bi3+ (6s2) and Bi5+ (5d10) ions, which have no unpaired electrons in their outer electronic shells, showed diamagnetic properties. In contrast, Bi0 (6s26p3), Bi+ (6s26p2), and Bi2+ (6s26p1) showed paramagnetic properties because of unpaired electrons in the 6p layer, contributing to the intrinsic magnetic moment. These detailed results have already been reported in [22, 37]. Furthermore, the Verdet constant increase of the Bi-doped silica fiber was faster than that of the SMF and Pb-doped silica fiber with the increase in the radiation dose (1.5–2.5 kGy). Therefore, it is believed that gamma rays clearly improve the magneto-optical properties of the BDF.
In this chapter, certain types of BRDFs, including Bi/Al, Bi/Pb, and Bi/Er co-doped optical fibers, were fabricated using the ALD and MCVD process. Then, the radiation effects on their optical properties were investigated. The fluorescence intensity and fluorescence lifetimes of the BRDFs at 1150 nm with low-dose radiation increased significantly, whereas they decreased with a further increase in the radiation dose. The merit Mα values of the BRDFs, a ratio of useful pump absorption to total pump absorption, decreased with an increase in the radiation doses. However, the Verdet constants in different doped fibers increased and reached saturation with the increasing radiation dose. The incremental increases of the Verdet constants for the Pb-doped and Bi-doped fibers were faster than those for the SMF with an increase in the radiation dose (1.5–2.5 kGy). Moreover, the Verdet constant decreased and the direction of Faraday’s rotation changed at low radiation doses. Hence, the increase in the Verdet constant increase for BDF is much faster than that of other fiber samples treated with high-dose radiation. All these results are of great significance for the study of the optical properties of BRDFs.
This work is supported by Natural Science Foundation of China (Grant Nos. 61520106014, 61975113, 61935002, and 61675125) and the Pre-Research Fund Project (6140414030203).
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