Fiber parameters of G. hirsutum, G. barbadense, G. arboreum, and G. herbaceum species.
\\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:{caption:"Highly Cited",originalUrl:"/media/original/117"}},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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\n | % word prod | \nDistribution | \nCommercial varieties | \nLength (mm) | \nTensile strength at zero-gauge (cN/Tex) | \nLinear density (mTex) | \n
---|---|---|---|---|---|---|
90 | \nCentral America | \nUpland cottons | \n25–32 medium | \n40 | \nUp to 200 | \n|
5–7 | \nSouth America, Egypt, Soudan, and Peru | \nEgyptian, Sea Island, and Pima cottons | \nSuperior to 33 long to extra long-staple cotton | \n55 | \n100–140 | \n|
3–4 | \nAfrica, Asia, and India | \nPakistan and India | \nInferior to 25 short fibers | \n35 | \n300 | \n
Fiber parameters of G. hirsutum, G. barbadense, G. arboreum, and G. herbaceum species.
Cotton fiber’s characterization is very complex and depends on the growing and harvesting conditions of the plant. It is very important for cotton breeders to understand the relationships existing between specific fiber properties, overall fiber, and yarn qualities [3]. All of these factors interact and are critical to the development of cottons that can compete in a global market.
\nUnderstanding these interactions will allow breeders for using the data more effectively for selecting the best cotton plants for developing a cotton variety with improved yarn quality [3, 4]. For this purpose, following properties of cotton fiber were described.
\nThe cotton fiber consists of a primary and secondary cell wall [5]. These latter are the key determinant of the cotton fiber growth and development and are primarily composed of cellulose (about 96% of pure cellulose: which is a naturally occurring crystalline carbohydrate polymer), hemicellulose, pectin, lignin, and structural proteins [2].
\nNumerous studies comprehensively describe the structure and development of cotton fibers [6, 7]. In brief, cotton fibers develop in three phases: initiation, elongation, and maturation through secondary wall thickening. The initiation of cotton fibers begins from the epidermal cells on the ovule surface to the elongation and the development of the primary cell wall. This latter, covered with a cuticle, continues to elongate until reaching the final fiber lengths. The secondary wall, which makes up 90% of fiber weight, consists of cellulose fibrils arranged in a layered helical structure. This layer contributes to the tensile properties of the cotton fiber and gives the final mechanical properties of the fiber. The final stage in the fiber development consists of the removal of moisture, during which the fiber collapses. Fibers are then converted from a cylindrical shape to a twisted ribbon. Figure 1 shows the changes in fiber length, diameter, and wall thickness during the development of the cotton fiber.
\nDifferent developmental stages of fiber.
Cotton fineness [8, 9, 10] is the linear density or weight per unit length of fiber. In fact, for a given fiber (that is assumed of a fixed density), its mass is proportional to its cross-sectional area:
\nThis relationship is used in the gravimetric definition of fiber fineness. The primary unit of fiber fineness is Tex (g/1000 m).
\nThe maturity [11, 12] of cotton fibers is the degree of thickening, which is defined as the ratio of the area of the cell wall to the area of a circle having the same perimeter as the fiber cross section. Thus, measuring the maturity of cotton fibers involves measuring the thickness of their secondary cell wall. The degree of wall thickening increases as the fiber matures.
\nBased on microscopic observations, wall thickness can be denoted by the degree of thickening (θ) as shown by the equation below [9]:
\nWhen the degree of thickening is equal to one, the fiber is then completely solid. When the value of θ is above 0.6, the fiber is considered mature. In the contrary, when θ is below 0.6, the fiber is considered immature.
\nMicronaire is an indicator of both fineness and maturity. Its value is determined by the measurement of the air permeability of a mass of cotton fibers under specified conditions. Micronaire [13, 14] measurement fails to properly distinguish fine and mature cotton fibers from coarser with lesser maturity. The empirical relationship combines the maturity, fineness, and micronaire:
\nwhere IM is the micronaire, Tt is the fiber fineness, and MR is the maturity ratio.
\nEnhancing fiber length is a complex issue because fiber samples from cotton bales contain a range distribution of fiber lengths [15].
\nThe prevailing environmental conditions during a growing season affect the length distribution of cotton fibers [16]. The length of cotton fibers fluctuates significantly not only among cultivars but also within a cultivar. It is because of the prevailing environmental conditions within the same plant due to position of the boll, within the same boll due to the flow of nutrients toward the developing individual seed, and within the same seed due to the positions of fibers on the seed. Besides, harvesting, ginning, and processing methods change the length distributions of cotton [17]. Determining the length of individual fibers is time consuming and difficult, so various methods for estimating fiber length have been devised. Most test methods and instruments for fiber length analysis measure the length and the weight of each group of fibers in order to determine the fiber length characteristics.
\nMechanical properties are the most important indicators for breeders to produce fibers that perform better in textile manufacturing and end-user [18]. Fiber mechanics is the study of the tensile, creep, relaxation, and fatigue properties, the key of the mechanical properties, for fibers and fibrous assemblies [19, 20]:
Tensile test
Tensile test is carried out to achieve fiber parameters such as strength, percent elongation, and initial modulus, and these important parameters are usually obtained after applying axial stress at a constant elongation rate till failure. The applied tensile load and elongation are recorded during the test for the calculation of the stress and the strain.
Creep test
Creep is a time-dependent deformation under a certain applied load. The rate of deformation is named the creep rate. It is the slope of the line in a creep strain vs. time curve.
Relaxation test
In the relaxation test, a constant strain is applied, and the stress is measured for a period.
Fatigue test
In a fatigue test [21], a fiber may be cycled over a wide range of frequencies under a variety of imposed extension rate conditions. The fiber is held between two clamps one of which connected to a vibration generator. An oscillatory force is applied to the fiber and is chosen as a percentage of the breaking force.
\nCotton fibers are not homogeneous in their physical properties. Their maturity, fineness, and lengths vary from fiber to fiber. Sometimes, even alongside the length of a fiber, there is a variation in physical properties. Thus, the mechanical properties are affected by these variabilities.
\nTable 2 regroups almost all the apparatus allowing to determine the physical and mechanical properties of cotton fibers.
\nPhysical properties | \n- Projection microscope - Airflow - Advanced Fiber Information System (AFIS ®) [22] - Vibroscope | \n||
- Goldthwait differential dyeing [23] - Double compression airflow measurement - Polarized light analysis - Causticaire - Centrifugal methods - Image analysis - Near-infra-red (NIR) spectrometry - X-ray fluorescence spectroscopy | \n|||
- Fineness and Maturity Tester (FMT) - Fibronaire - Cottonscope [24] - Advanced Fiber Information System (AFIS ®) | \n|||
- Zweigle Sorter - WIRA fiber length machine - Fibrograph - Almeter - High Volume Instruments (HVI ®) [23] - Advanced Fiber Information System (AFIS ®) | \n|||
\n | Single fibers | \nBundles | \n|
Mechanical properties | \n- Mantis Single Fiber Tester [25] - Favimat [26] - Universal FiberTesting Machine (UFT ®) [20] - Dynamometer MTS | \n- Stelometer - HVI ® - Dynamometer MTS | \n|
- Dynamometer MTS - UFT ® | \n- Dynamometer MTS | \n
Apparatus allowing measuring fiber physical and mechanical properties.
To determine the bijective relationships between single and bundle cotton fibers, 12 bales of cotton were selected based on their distinct physical properties. These cover a large panel of cotton micronaire, tenacity, and lengths. Prior to testing, all cotton samples were conditioned for at least 48 h at 65 ± 2% RH and 21 ± 1°C.
\nThe mean values of fineness, maturity, micronaire, and length were determined by the Fineness Maturity Tester and Micromat. The results are shown in Figure 2a and b.
\n(a) Tenacity vs. micronaire. (b) length vs. micronaire.
The principle of the AFIS consists of single fiber measurements with an opto-electronic sensor. Fiber creates signal/impulse converted to an electrical signal, which is analyzed and evaluated by computer. For instance, for length values, the main measurements include: mean length, length upper percentiles, length CV%, short fiber content (defined as the percentage of fibers less than 12.7 mm in length), upper half mean length (UHML), and upper quartile length (UQL).
\nHowever, for determining the length, Zweigle sorter method was used. It allows sorting fiber length into groups. It is based on using a Johannsen-Zweigle apparatus (Figure 3). The device consists of two steel comb fields to align and straighten the fibers. For cotton sample testing, the combs are spaced from each other for a distance of 4 mm, and the weight of the test specimen is 100 mg. Once prepared, the fibers go through repeated drawing and doubling processes to form straight and parallel bundle of fibers. Length intervals are obtained allowing classification of the fibers into groups. In order to obtain a mass distribution of sample, the fibers of each group are then weighed. The length’s interval of each group is determined by the spacing of the combs. We must have at least 10 sample groups extended on the longest fiber. Thus, the longest fibers are drawn and weighed first, followed by the shorter.
\nZweigle sorter apparatus of the Laboratoire de Physique et Mécanique Textile (LPMT).
For measuring single fiber properties, Favimat (Textechno Herbert Stein GmbH and Co. KG, Möchengladbach, Germany; Figure 4) was used. The typical testing methods of the Favimat are the static tensile test, linear-density (fineness) measurement, and measurements of crimp extension, crimp stability, and number of crimps.
\nThe Favimat device for tensile and linear density measurements.
The main principle is that both single fiber ends are clamped between two sets of jaws. The displacement is insured by a constant speed motor with interchangeable equipment to vary the rate of elongation. For the tensile tests, the data of the load and elongation are transferred to a computer to be plotted and analyzed.
\nUniversal fiber testing machine (UFT) was also used. This apparatus allows to carry out the testing of single fibers in tensile, creep, relaxation, and fatigue. It was developed by Bunsell and Hearle in the 1970s. Figure 5 shows the UFT device of the LPMT laboratory.
\nUFT device of the LPMT laboratory.
In addition, the dynamometer MTS with a 2 N sensor was used for the single fiber creep and relaxation tests. The fibers are glued with a cyanoacrylate glue in the extremities of a 15-mm diameter paper. Once placed in the two jaws of the dynamometer MTS (Figure 6), paper is cut on its extremities to allow the extension of the fiber.
\nSingle fiber disposition in the dynamometer MTS.
For bundle testing, Pressley clamps were used and placed on a dynamometer device. An attachment system was compatible with the dynamometer MTS, and the jaws were designed in our laboratory as shown in Figure 7. The sensor used for the bundle fiber test had a 2-kN sensibility.
\nPressley clamps in the dynamometer device.
Preparation of the samples in the Pressley jaws is similar to the one used for the Stelometer measurements. In fact, the specimen is pulled manually through the teeth of a comb several times to straighten fibers and remove all the neps. A flat bundle of fiber is placed in the device (Figure 8) and is fixed with in a special vise, which provides a pre-stressing load at a 100-g tension. A torsion spring ensures uniform tightening of the clamps. The ends of the sample are then cut off with a special knife, and finally, the clamps are placed in the instrument.
\nStelometer device of the LPMT.
Tests allowed to determine the physical properties of cotton fibers are good indicators of the global behavior of fiber. In fact, many parameters can be determined from these tests (such as E-initial modulus, \n
Analogical models are represented based on the assembly of simple mechanical elements (springs, dashpots, skidding blocks, or stopping blocks) having the same responses to those expected by the real material. These models are very useful to clarify how the fiber (single or bundle) behaves. The most common elements are shown in Table 3 [27]. The mechanical elements can be assembled both in series or in parallel or in mixed groups (networks). Thus, more complex mechanical responses can be simulated to illustrate the behavior of the material submitted to the tensile, creep, relaxation, and fatigue tests [21, 28].
\nStandard linear solid models.
Cotton fibers are viscoelastic. Creep and stress relaxation tests demonstrate this characteristic. In creep test, a constant stress is maintained on a specimen while its deformation is monitored as a function of time, and deformation increases with the time. In stress relaxation test, a constant deformation is maintained while the stress on the specimen is monitored as a function of time, and stress decreases with time.
\nThe classical viscoelastic constitutive models are represented by Maxwell and Kelvin-Voigt models using springs and dashpots to simulate elasticity and viscosity, respectively. The respective equations are:
\nIn the case of the Maxwell model, the behavior is modeled by a spring and a dashpot connected in series:
\n\n
Eq. 6 gives response of the Maxwell model. For example, for the creep and relaxation tests, where a constant strain (ε = ε0) and a constant stress (σ = σ0) at t = 0 are applied, respectively, the respective following responses are obtained:
\nIn the Kelvin-Voigt model, the spring and the dashpot are connected in parallel:
\n\n
The Kelvin-Voigt response of the creep test at a constant stress σ0 at t = 0 is:
\nAmong these 12 varieties of cotton fibers, C7, C42, and C55 were selected. In fact, C7 and C42 have the same micronaire but showed different tenacities and lengths. However, C7 and C55 showed the same length and tenacity but different micronaires. Classification per length using the Zweigle Sorter was carried out in order to get more information about the variability of tensile properties across various length classes. The classes found for each cotton are shown in Table 4.
\n\n | [38–36] | \n[36–34] | \n[34–32] | \n[32–30] | \n[30–28] | \n[28–26] | \n[26–24] | \n[24–22] | \n[22–20] | \n[20–18] | \n
---|---|---|---|---|---|---|---|---|---|---|
C7 | \n\n | \n | \n | \n | \n | \n | \n | \n | \n | \n |
C42 | \n\n | \n | \n | \n | \n | \n | \n | \n | \n | \n |
C55 | \n\n | \n | \n | \n | \n | \n | \n | \n | \n | \n |
Length classes for cottons C7, C42, and C55 (the shading illustrates the length classes).
Single and bundle tests are carried out for each length class of each cotton fiber.
\nTensile tests of single fibers can be undertaken with the Favimat, which determines the linear densities. This latter is measured according to the vibroscopic testing principle. Two stickers are attached in extremities of the cotton fibers and placed between the two jaws of the Favimat. Test parameters are as follow, and the test results are shown in Table 5:
Test speed: 5 mm/min.
Gauge length: 15 mm.
Sensor: 210 cN.
Pretension: 0.06 cN/Tex.
Nominal linear density: 10 dTex.
\n | Elong max (%) | \nElong rupt (%) | \nForce max (cN) | \nWork of rupt (cN cm) | \nTenacity (cN/tex) | \nLinear density (dtex) | \nTime (s) | \nSpecific modulus E | \n
---|---|---|---|---|---|---|---|---|
C7 [38–36] | \n7.12 | \n7.62 | \n5.34 | \n0.26 | \n94.85 | \n0.58 | \n13.83 | \n13.54 | \n
C7 [36–34] | \n8.16 | \n8.30 | \n5.35 | \n0.30 | \n72.48 | \n0.76 | \n15.04 | \n9.11 | \n
C7 [34–32] | \n7.66 | \n7.83 | \n4.84 | \n0.27 | \n58.18 | \n0.84 | \n14.19 | \n7.81 | \n
C7 [32–30] | \n8.50 | \n8.64 | \n4.78 | \n0.28 | \n62.33 | \n0.78 | \n15.70 | \n7.95 | \n
C42 [34–32] | \n7.23 | \n7.37 | \n4.09 | \n0.21 | \n42.86 | \n1.04 | \n13.40 | \n6.00 | \n
C42 [32–30] | \n9.02 | \n9.17 | \n4.42 | \n0.26 | \n42.28 | \n1.08 | \n16.71 | \n4.96 | \n
C55 [36–34] | \n9.24 | \n9.38 | \n6.90 | \n0.41 | \n99.96 | \n0.71 | \n16.99 | \n11.48 | \n
C55 [34–32] | \n8.04 | \n8.18 | \n5.00 | \n0.29 | \n65.79 | \n1.04 | \n14.88 | \n7.89 | \n
C55 [32–30] | \n7.66 | \n7.80 | \n5.65 | \n0.29 | \n85.05 | \n0.68 | \n14.15 | \n11.36 | \n
Tensile tests results for some length classes of the cottons C7, C42, and C55.
Creep and relaxation tests of single fibers were done with a dynamometer MTS.
\nTest parameters are as follow:
Test speed: 5 mm/min.
Gauge length: 15 mm.
Sensor: 200 cN.
Results (Figure 9) showed that fibers behaved similarly during the creep test for the length class [34–32] for the cottons 7, 42, and 55. This result is in coherence with the values of the initial modulus E determined from the tensile tests for the same length class.
\nCreep test responses for cottons C7, C42, and C55 for length class [34–32].
We can also note that the strain increased and asymptotically approached the value of Ln(σ0/E) when t tended to infinity. The response of this model to an applied stress is characterized by a fast-increase part explained by the fact that the stress is at first carried entirely by the viscous element. The second part characterized by a very slight increase explains the elastic element in the continuous elongation of the viscous element. The transition time between the two parts represents the creep time constant, t, which is equals to η/E, where η is the viscosity and E is the initial modulus given by the tensile test at a given constant rate of extension [28].
\nWe concluded that the creep response was viscoelastic and therefore that we could apply a Kelvin-Voigt model.
\nAs for breeders and geneticists, new cotton varieties developed either by the conventional techniques or by genetic engineering, and detailed characterization of physical and mechanical properties of cotton fibers is essentially required. These properties must be tested fiber-to-fiber, which is very lengthy, tedious, and expensive procedure. Finding a relationship between single and bundle fiber models would therefore be desirable. The aim is then to be able to test these varieties in bundles and to directly determine the single properties.
\nFor bundle testing, the tests were carried out with a speed of 50 mm/min.
\nData acquired from the tensile tests are:
Load and elongation at peak.
Initial modulus.
Work of rupture.
The examination of the behavior from the load-elongation curve (Figure 10) revealed that we could approximate their shapes to a right-angled triangle, with the base being the elongation and the height being the peak load.
\nResponse of the bundle fibers to tensile tests.
Evaluation of cotton fiber tensile properties serves multiple purposes [29]. The results obtained enable to estimate the performance of raw materials during the transformation procedures of fibers. It is also used to predict the tensile properties of spun yarns or woven textiles. Fiber bundle tensile tests can appear to satisfy the objective because of their relationships with tensile properties of yarn. However, this relationship can be rapidly expected in assemblies of parallel fiber factors such as the degree of fiber-to-fiber interactions and twist contribute to fiber bundle strength.
\nRegarding creep test results for bundles, we are working on to find out the corresponding models of each cotton variety and comparing them with the single ones. We estimate the bijective relationships existing between the two cotton fiber testing dispositions.
\nBeing among the finest natural fibers, cotton is subjected to a variety of characterization tests. These latter can be classified into moisture absorption, electrical, thermal, physical, and mechanical. Cotton breeding programs must deliver fibers that better perform in textile manufacturing in order to compete effectively with international growths of cotton and with the various man-made fibers. In fact, the performance of fibers in textile processing and the quality of the final fabric are highly dependent on the mechanical properties of raw fibers.
\nTensile properties of yarns and fabrics depend on both complex fiber arrangements (including length, diameter, friction, etc.) inside the yarn and fabric structure and on the tensile properties of fibers. Thus, it is necessary for the breeders to know about the complex relationships between the fiber arrangements parameters.
\nIn this chapter, we showed that the mechanical behavior of fibers can be analogically modeled and that we assimilate the cotton single fibers creep response to a Kelvin-Voigt one. Nevertheless, the relationship between fiber-to-fiber testing and bundles remains to be clarified through further research.
\nMitigating hot and cold injuries is a complex problem and has been shown to have significant links to a number of individualized factors, to include race, gender, job specialty, and geographic origin [1, 2]. There are many other individualized elements (e.g., fitness, body composition, and genetics) that are intuitively linked to these health outcomes; however, there is a lack of adequate data to scale that sufficiently addresses these issues.
\nThe history of characterizing heat exchange and thermoregulatory functions in humans can be traced back to the late 1770s; where British military physiologist, Sir Charles Blagden conducted descriptive studies of man, dog, and beef steak responses in a hot room [3]. Mathematically describing heat exchange theory has roots in physics and with the development of the laws of thermodynamics and heat exchange, specifically as described in Fourier’s law [4] a mathematical expression of the dynamics of heat balance in solids, simplified as:
\nwhere \n
Key work by Pennes in 1948 [5], reported measured temperatures of tissue and blood at the forearm and enabled the creation of the bioheat transfer equation. This equation has proven to be a key underlying basis of future models, seen as:
\nwhere
Conceptually, heat exchange between the human and the environment was first described by Lefevre in 1911; where he characterized the human as a sphere with an internal core that exchanged heat through the shell into the environment [6]. In 1934, Burton applied Fourier’s law, presenting this exchange mathematically and describing the human as one uniform cylinder in what is considered by many as the first visual conceptualization of human thermoregulatory modeling [7].
\nRepresentation of the human in a thermoregulatory model is most often done by sectioning the human into nodes, segments, and elements; typically using one of four different designs, (1) one-node, (2) two-node, (3) multi-node, or (4) multi-element [8]. An example of the difference between these designs is shown in Figure 1; while the multi-element approach is more realistic human shape (e.g., finite analysis distribution). Typically each node represents an independent layer with unique thermal properties, each segment represents a section or grouped section of an area of the body, and each element represents multiple thermal components that make up the whole body (often more geometrically accurate to the shape of the human).
\nExample of model designs.
One node models are essentially empirically derived and do not include elements within the thermoregulatory response system. There are several one node thermoregulatory models that have been used extensively over time to predict core body temperature and thermal discomfort within a given environment [9, 10, 11, 12].
\nSimple two-node models describe specific thermodynamic responses of a single segment, typically separated into concentric core and shell nodes. They have often been used examine thermal discomfort and physiological responses, to include the work by Gagge and Nishi [13, 14, 15], and several others [16, 17, 18, 19]. Two node model approaches have been used where the two node design was applied to multi-segments [20, 21, 22, 23]. Multi-node models are essentially expanded versions of the two-node methods with additional shells or layers within them where the heat balance is calculated for each layer. Multi-node models, with both single- and multi-segment designs have become the more prevalent approach. The first multi-node model was developed by Crosbie et al. [24] and has been followed by many since [25, 26, 27, 28, 29]. Notable is the work of Solwijk and Hardy [30, 31, 32, 33], where they first introduced the concepts of temperature set points and negative feedback in a controlled theory design. Their work has been built upon by many researchers over time [34, 35, 36, 37, 38, 39, 40, 41, 42]. The first multi-element model was originally published in 1961 by Wissler, and later improved upon [43, 44, 45]. Additional multi-element models include work by Smith [46], with the first three-dimensional (3D) transient multi-element model. As computation methods improved, a series of improvements has led to more realistic and complex models [8, 47, 48, 49, 50].
\nWhile the majority of these models were developed with the intent of characterizing thermoregulation in various environments; several have been designed specifically to address cold environments or thermoregulatory events that specifically address cold issues (e.g., finger, hand, foot temperatures). With the intricacies of human response to cold, studies have focused on extremities, the specific areas most subject to cold injuries. One of the first attempts was by Molnar in 1957, used a heat balance approach to study hand temperature responses to cold [51]. This work was followed by work focused on finger freezing points [52, 53, 54, 55, 56, 57] and whole hand modeling [58, 59]. Specific models have also been developed of the foot [60], toes [61], and facial tissues [62, 63]. Cold survival models have been developed over time to make predictions in both open air and submerged environments [64, 65, 66, 67, 68].
\nCharacterizing cold related injuries is fairly complex, as the responses to cold have higher individual variability when compared to heat related injuries. From a clinical perspective, cold related injuries can be broadly divided into three categories: frostbite, nonfreezing cold injuries, and hypothermia. In addition, each of these has varying levels of severity and subcategories associated to them.
\nFrostbite is below the point at which skin tissue begins to freeze. While 0°C (32°F) is traditionally considered the freezing point of water, the freezing point of skin is understood to be marginally lower due to electrolytes [69]. Observed freezing points range from as low as −4.8°C to as high as −0.6°C [69, 70].
\nNonfreezing cold injuries include an array of injury events where tissue freezing has not occurred but damage occurs. The level of severity of nonfreezing injuries is determined by the temperature, duration, and wetness of the exposure to the tissue. Four of the more common specific types of nonfreezing injuries include immersion (trench) foot, chilblain, cold urticaria, and cold-induced bronchoconstriction [71].
\nImmersion foot is a nonfreezing injury. The foot presents swollen, the skin is red initially but as severity increases the skin becomes lower in oxygen saturation and becomes cyanotic (purple, bluish discoloration) [69, 71]. Immersion foot is most often reported after tissue have been exposed for extended periods of time to non-freezing temperatures, between 0 and 15°C (32–60°F) [71]. The term ‘immersion’ itself refers to when the foot is actually immersed in water when the foot is wet within boots for sustained periods of time [69, 71].
\nChilblain is a fairly common nonfreezing injury to the skin. It can occur during 1–5 hours of temperatures below 16°C (60°F) [69]. Cold urticaria is expressed as a quick onset of redness, swelling and itchiness of the skin in response to short-term exposure (i.e., minutes) to cold environments [71]. Cold-induced bronchoconstriction is a physiological response where an individual’s airways are narrowed during exercise in cold environments [69, 71, 72, 73].
\nHypothermia is a broad category of cold injury and is clinically described to be the point at which core body temperature has dropped below 35°C (95°F) [74]. However, hypothermia is more specifically defined with four levels of severity; where normothermia (normal temperature level) is approximately 37°C (98.6°F), mild hypothermia is between 91.4–95°C (33–35°F), moderate hypothermia being 85.2–89.6°C (29–32°F), and severe hypothermia being 56.7–82.4°C (13.7–28°F) [69, 71]. Figure 2 outlines specific core temperature reference points associated with physiological responses using work by Castellani et al. [69] and Pozos and Danzl [74] and described in Army Guidance [75].
\nThe range of human core temperatures and associated physiological responses [
The human body is capable of maintaining thermal balance while operating within a wide range of temperatures. The human system generally maintains an internal core temperature (Tc) of approximately 37°C. Due to natural circadian rhythm, Tc fluctuates ~0.5°C daily. However, Tc can fluctuate based on physical activity or environmental conditions, and may range from 36.0–40.0°C. The microenvironment created between human skin and clothing typically must remain within 28–30°C to maintain thermal homeostasis at rest [45]. This microenvironment changes significantly with physical activity due to metabolic heat production and air movement.
\nHumans have an internal control system, primarily the preoptic area of the anterior hypothalamus, responsible for maintaining healthy body temperature. The hypothalamus uses feedback from two main sources, the skin and the blood. When temperature changes (hot or cold) are identified by either of these two sources, impulses are sent to the hypothalamus which in turn directs physiological changes to compensate for these temperatures. To protect from cold or heat injury, the human body attempts to either generate or dissipate heat to stay warm or cool off. Heat production is a natural process for humans and is a function of metabolism, oxidation of foods, and muscular activity. Heat transfer between the human and environment occurs via four pathways: conduction, convection, radiation, and evaporation. This heat exchange process is typically referred to as heat or thermal energy balance, and can be described in the heat balance equation:
\nwhere S is heat storage; M is metabolic rate; W is work rate; R is radiation; C is convection; K is conduction; and E is evaporation. Radiation is heat that is transferred via electromagnetic waves (e.g., solar radiation). Conduction is heat transfer due to the body’s direct contact with a solid object (e.g., touching a cold surface). Convection is heat transfer between the body and a fluid such as air or water. Evaporation is heat loss to the environment due to the phase change from liquid to vapor, typically associated with evaporation of sweat and respiratory water.
\nHyperthermia is when heat gain exceeds heat loss; while hypothermia occurs when body temperature drops below normal levels as heat production is inadequate to compensate for the rate of heat loss to the environment [77].
\nVasoconstriction and vasodilation are the two key physiological responses of how heat transfer is regulated from the body to the periphery [78, 79]. Vasoconstriction is the constriction of blood vessels and occurs in response to cold environments to reduce the amount of blood flow to the skin. Vasoconstriction protects the internal organs from cold exposure but increases cold injury risk in the extremities due to lower blood flow and lower skin temperatures. Vasoconstriction in effect creates a two-layer distribution of body temperature; a cold outer shell surrounding a warmer core. The colder outer shell reduces heat loss to the environment by reducing the temperature gradient between the skin surface and the environment, and a colder surface radiates less heat.
\nVasodilation is essentially the opposite of vasoconstriction; where blood vessels open to allow increased blood flow across the body and out to the extremities to enable increased heat dissipation [78, 79]. During these responses, there are other associated physiological responses that help compensate for the increased skin blood flow (e.g., increased heart rate and cardiac output).
\nThe extremities are more affected by cold exposure than other parts of the body. When the human body cools, blood flow is reduced to the extremities (i.e., the hands and feet) decreasing the amount of warm blood flowing to these areas. It is a challenge to protect the hands and feet as they have lower metabolic heat production of the hands and feet due to their inherently small muscle mass and large surface area to mass ratio.
\nFrom a functional perspective, the balance of control within the human system depends on the response to cold exposure and interaction between peripheral (skin) and core body temperatures with the central nervous system (CNS) and the various physiological responses (Figure 3); [74].
\nPeripheral (skin) and core temperature influence on central nervous system (CNS) and physiological outcomes.
Clothing has long been used to provide protection from environmental elements (heat, cold, etc.) or physical or biological hazards (e.g., rocks, thorns). Clothing properties and requirements vary widely among users and use cases. A single clothing ensemble cannot protect an individual from the extremes of the temperature spectrum of earth, being approximately −89°C at its coldest and 58°C at its warmest. However, clothing is a toll to protect each end of this spectrum of environmental extremes [80]. However, protections must be based on use cases to achieve the desired thermal comfort. For example, protective equipment for American football players (i.e., pads and helmet) is vastly different than protective equipment worn by soldiers (i.e., body armor, ballistic helmet). It should be noted that added protection may increase the thermal burden to wearers, and thus increases risk of heat injuries [81, 82, 83].
\nIt is critical to understand the clothing option tradespace in order to predict and prepare for the impact clothing has on protecting or impairing human health. That is to say, the selection of the proper clothing, requires an understanding of how the human (physiology, anthropology, etc.), the anticipated activities (i.e., work rate, length of exposure and metabolic heat production), the work environments (temperature, humidity, etc.), and the biophysical properties of clothing worn (heat transfer performance) will interact in each workplace scenario.
\nClothing protects the wearer from environmental threats, but may impose a level of thermal burden. Both the biophysical resistances (thermal and evaporative) and spectrophotometric (reflectance, absorptivity, and transmittance) properties of clothing can have a significant influence on the impact of the environment on the wearer. Measurements of the biophysical properties of clothing can be used to model the impacts on thermal sensation (e.g., thermal comfort) and thermoregulatory responses (e.g., heat strain, cold protection). The thermal and evaporative resistances, wind effects, and spectrophotometric properties of the clothing are critical measurements for this purpose.
\nSweating thermal manikins have long been used to provide biophysical measures of clothing and equipment worn by the human [84]. While direct biophysical comparisons can be helpful, i.e., comparing one ensemble’s value to another [85], a more informative approach is to combine these measured values with thermoregulatory modeling. Models enable the prediction of thermoregulatory responses based on different individuals, as well as varied environments, clothing, or activity levels.
\nThe current standard for thermal manikin testing calls for two fundamental measures: thermal resistance (
Each ensemble should be tested using chamber conditions from the American Society for Testing and Materials (ASTM) standards for assessing
Variable (unit) | \nSkin/surface temperature ( | \nAmbient temperature ( | \nRelative humidity (RH, %) | \nWind velocity ( | \nSaturation (%) | \n
---|---|---|---|---|---|
\n | \n35 | \n20 | \n50 | \n0.4 | \n0 | \n
\n | \n35 | \n35 | \n40 | \n0.4 | \n100 | \n
American Society for Testing and Materials standard chamber and manikin conditions for testing thermal
Thermal resistance (
where Ts is surface temperature and Ta is the air temperature, both in °C or °K. Q is power input (W) to maintain the surface (skin) temperature (Ts) of the manikin at a given set point; A is the surface area of the measurement in m2. These measures of
where IT is the total insulation including boundary air layers. Evaporative resistance (Ret) is heat loss from the body in isothermal conditions (Ts ≈ Ta), described as:
\nwhere Psat is vapor pressure in Pascal at the surface of the manikin (assumed to be fully saturated), and Pa is ambient vapor pressure, in Pascal, of the chamber environment. Measures of Ret can then be converted to a vapor permeability index (im), a non-dimensional measure of water vapor resistance of materials defined as:
\nIn order to use the biophysical measures, i.e., measures of
Clothing properties and wind coefficients are critical inputs to a number of predictive mathematical models [10, 11, 93, 94], as they use these values to describe wind-related effects, such as intrinsic insulation (
where
where
True measures of
While these estimation methods have been studied and produce acceptable variance between estimated and direct measured results [96], there are questions whether estimates remain acceptable for clothing insulation outside typical cold weather clothing insulation ranges, e.g., 0.2–1.7 clo [97].
\nMost clothing-based thermal models, by design, predict human thermoregulatory responses to various environmental conditions and therefore require quantitative insights into the change in clothing properties with changes in wind velocity. Furthermore, elements of wind can significantly influence physiological responses and injury outcomes in cold environments due to wind chill effects [69, 98, 99]. There has been work to develop that relates exposure time to predicted injury (e.g., frostbite) likely to occur due to temperature and levels of wind speed exposure [98].
\nMathematical models can predict the human thermal response (e.g., metabolic heat production, core body temperature (
Rational modeling incorporates equations that describe heat balance and thermoregulatory processes [100]. Two fundamental equations are used to describe internal heat balance and for heat exchange between skin and environment. One equation outlines the temperature gradient change from core to skin and can be seen as:
\nwhere \n
The second equation describes heat exchange from the skin surface to the environment as:
\nwhere λ is the tissue heat conductivity (W m−1 °C−1), T is tissue temperature (°C), \n
Rational models of thermoregulatory processes usually include equations for the controlling signals of the thermoregulation system and equations for thermoregulatory actions such as sweating, vasodilation, vasoconstriction, and shivering.
\nUnderstanding the interplay between each of the different layers of the human (grossly consisting of core, muscle, fat, and skin) along with clothing and air layers within clothing is only the first step to modeling the human’s response in a given environment. Figure 4 shows the rational basis behind the SCENARIO model where the human is mathematically represented as one multi-layer cylinder, based on the relationship of the layers of the human, their respective physiological responses, and clothing [93, 94].
\nFundamental rational basis (SCENARIO model) [
Empirical models are mathematical representations of data, often using statistical methods such as regression or correlational analysis. An example model is the Heat Strain Decision Aid (HSDA), empirically derived by the U.S. Army from an extensive database of human studies that incorporates the biophysics of heat exchange [10, 11, 101] and predicts core temperature, maximum work times, sustainable work-rest cycles, water requirements, and the estimated likelihood of heat casualties. This model has been used to derive guidance and doctrine for military [102] and fluid intake guidance for the public [103]. The basis of HSDA includes both principles of heat exchange along with empirical predictions of physiological responses. Collectively 16 inputs from four elements (individual characteristics, physical activity, clothing biophysics, and environmental conditions) are used to mathematically predict the rise in core body temperature during physical activity [10].
\nOriginally developed by Holmér [104], a simple calculation was adopted by the International Organization Standardization (ISO) technical report (ISO 11079) [105], as an evaluation metric of the insulation required (IREQ) for given environments and activities to compare ensemble performance. The IREQ method functionally describes the concept for balancing the heat exchange between the human and the environment, and simplified as:
\nwhere
The IREQ equation illustrates the rational balance between thermal insulation and heat transfer, seen as:
\nor more formally as:
\nwhere
This method also determines the minimum and neutral IREQ (IREQmin and IREQneutral), and describes amounts of insulation needed to maintain thermal balance (minimum) and to maintain an equilibrium balance (neutral). The ISO 11079 also outlines general scenarios for the minimum required insulation (IREQmin) for multiple work intensities and environments. Collectively this method provides a simple method for evaluating the effectiveness of specific cold weather clothing at protecting from cold injuries [106].
\nWhen developing a cold-based thermal model there are a number of physiological, environmental, and biophysical parameters that can and should be considered. Particular attention should be paid to the extremity temperatures blood flow and metabolic heat production.
\nAs blood flow is a major component to the overall movement of heat, it is important to be able to predict blood flow to the muscle, skin, and distribution of blood flow to these regions within the body. Table 2 outlines some historical methods used in models for predicting each of these elements.
\nPrediction | \nEquation | \nUnits | \nReferences | \n
---|---|---|---|
Cutaneous blood flow (\n | \n\n\n | \nmL 100 mL tissue−1 min−1\n | \n[79, 107, 108, 109, 110, 111, 112, 113, 114, 115] | \n
Skin vasodilation (dilat) | \n\n\n | \nL h−1\n | \n[33] | \n
Skin vasoconstriction (stric) | \n\n\n | \nL h−1\n | \n[33] | \n
Skin blood flow (\n | \n\n\n | \nmL min−1\n | \n[116] | \n
Local blood flow (\n | \n\n\n | \nL h−1\n | \n[33] | \n
Muscle blood flow (\n | \n\n\n | \nL h−1\n | \n[33] | \n
Muscle blood flow (\n | \n\n\n | \nmL min−1\n | \n[116] | \n
Methods for predicting skin blood flow in thermoregulatory models.
Note:
Shivering is where, in response to cold exposure, muscles involuntarily contract rhythmically off and on in an attempt to increase body temperature [74]. During cold exposure the shivering response is a critical element to model, as the production of heat protects the body core temperature despite skin to the ambient heat loss. Table 3 outlines some of the modeling approaches that have been used to predict the shivering response as they relate to the total metabolic rate (
Prediction | \nEquation | \nUnits | \nReferences | \n
---|---|---|---|
Total shivering (TOTMshiv) | \n\n\n | \nkcal h−1\n | \n[37] | \n
Maximal shivering (Shivmax) | \n\n\n | \nmLO2 kg−1 min−1\n | \n[37] | \n
Metabolic rate of shivering (Mshiv) | \n\n\n | \nkcal h−1\n | \n[30] | \n
Metabolic rate of shivering (Mshiv) | \n\n\n | \nkcal h−1\n | \n[117] | \n
Metabolic rate to open air (M1) | \n\n\n | \nW m−2\n | \n[118] | \n
Total metabolic rate (M2) | \n\n\n | \nW m−2\n | \n[118] | \n
Total metabolic rate (M) | \n\n\n | \nW kg−1\n | \n[119] | \n
Metabolic rate of shivering (Mshiv) | \n\n\n | \nW m−2\n | \n[120] | \n
Methods for predicting shivering related model calculations.
Note:
An individual’s metabolic heat production can be estimated at rest and during activity using the assumed basal rate of 58.2 W/m2 [121] and the estimated metabolic equivalents (METS) of activity; where 1 MET is resting. Ainsworth et al. [122] outlines a wide range of activities and their associated MET level for reference. However, there are metabolic rate estimation methods available based on energy costs of standing or walking (Table 4). Recently work has also been published that makes corrections to some of these prediction methods specific to traveling over snow terrain [123].
\nPrediction | \nEquation | \nUnits | \nReferences | \n
---|---|---|---|
Metabolic rate | \n=1.44 + 1.94· | \nW kg−1\n | \n[124] | \n
=3.5 + 6· | \nmLO2 kg−1 min−1\n | \n[125] | \n|
=17.7–18.138· | \nmLO2 kg−1 min−1\n | \n[126] | \n|
=1.4 + 0.42· | \nW kg−1\n | \n[127] | \n|
=1.5· | \nW | \n[128] | \n|
= | \nl O2 min−1\n | \n[129] | \n
Methods for predicting metabolic rates during walking or standing.
Mathematical models and decision aids are tools for inspiring advancements within the field of thermophysiology, and for providing solutions to help mitigate injury risk.
\nScientifically based models have been used in the development of public [97, 98, 103, 104, 130, 131, 132] and military guidance [75, 131, 133], for forensic assessments [134, 135, 136, 137, 138, 139, 140], as well in the creation of operational tools for survival [141, 142]. Notably, the use of Xu and Werner’s six cylinder model [41] was used to develop the Probability of Survival Decision Aid (PSDA), a computer model used to predict hypothermia and dehydration impact on functional time (i.e., duration of ability for useful work), and survival time while exposed to marine environments [67, 143, 144]. The PSDA model is underpinned by the rational principles described herein and the outputs are provided in a customized graphical user interface. This tool has been transitioned for use by Search and Rescue (SaR) personnel and continues to be refined and verified based on real-world feedback and data collected [144].
\nThere is a need for continued advancement in the development of individualized modeling methods such as finite element models as well as providing models and decision aids that can be used in dynamic settings and for complex scenarios with prolonged durations. Additionally, inclusion of probabilistic and statistically based risk factors should be used as elements that help improve individualized predictions. The accessibility of the information from these tools continues to be a challenge for the scientific community. While providing usable information to the public, military, and other user communities should be the ultimate goal of these work efforts; feedback from these communities should be translated back to the scientists to ensure relevant improvements are made from real-world information.
\nThis work is dedicated to the memory of Dr. Eugene H. Wissler (1927–2018). His pioneering efforts modeling heat transfer in the human body provided critical ground work that continues to be emulated by researchers.
\nThe authors would also like to thank Dr. Scott Montain for oversight and review of this chapter.
\nThe authors have no conflicts of interest to declare. Funding for this work has been provided by U.S. Army Medical Research and Materiel Command (USAMRMC), Military Operational Medicine Research Program (MOMRP).
The views expressed in this paper are those of the authors and do not reflect the official policy of the Department of Army, Department of Defense, or the US Government.
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\n\nNote: All data represented above was collected by IntechOpen from 2013 to 2017.
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Gnanasekaran and Suresh Mayilswamy",coverURL:"https://cdn.intechopen.com/books/images_new/10506.jpg",editedByType:"Edited by",editors:[{id:"247421",title:"Dr.",name:"Samson Jerold Samuel",middleName:null,surname:"Chelladurai",slug:"samson-jerold-samuel-chelladurai",fullName:"Samson Jerold Samuel Chelladurai"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10629",title:"Advances in High-Entropy Alloys",subtitle:"Materials Research, Exotic Properties and Applications",isOpenForSubmission:!1,hash:"eef8e329dc2559a9dbe5f1522ec690e3",slug:"advances-in-high-entropy-alloys-materials-research-exotic-properties-and-applications",bookSignature:"Jiro Kitagawa",coverURL:"https://cdn.intechopen.com/books/images_new/10629.jpg",editedByType:"Edited by",editors:[{id:"210570",title:"Prof.",name:"Jiro",middleName:null,surname:"Kitagawa",slug:"jiro-kitagawa",fullName:"Jiro Kitagawa"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9991",title:"Iron Ores",subtitle:null,isOpenForSubmission:!1,hash:"f1b2c288312233e1be62cd01c7e74fec",slug:"iron-ores",bookSignature:"Volodymyr Shatokha",coverURL:"https://cdn.intechopen.com/books/images_new/9991.jpg",editedByType:"Edited by",editors:[{id:"111000",title:"Dr.",name:"Volodymyr",middleName:null,surname:"Shatokha",slug:"volodymyr-shatokha",fullName:"Volodymyr Shatokha"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10412",title:"Transition Metal Compounds",subtitle:"Synthesis, Properties, and Application",isOpenForSubmission:!1,hash:"bd7287b801dc0ac77e01f66842dc1d99",slug:"transition-metal-compounds-synthesis-properties-and-application",bookSignature:"Sajjad Haider and Adnan Haider",coverURL:"https://cdn.intechopen.com/books/images_new/10412.jpg",editedByType:"Edited by",editors:[{id:"110708",title:"Dr.",name:"Sajjad",middleName:null,surname:"Haider",slug:"sajjad-haider",fullName:"Sajjad Haider"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:126,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"60680",doi:"10.5772/intechopen.76082",title:"Environmental Contamination by Heavy Metals",slug:"environmental-contamination-by-heavy-metals",totalDownloads:16255,totalCrossrefCites:188,totalDimensionsCites:408,abstract:"The environment and its compartments have been severely polluted by heavy metals. This has compromised the ability of the environment to foster life and render its intrinsic values. Heavy metals are known to be naturally occurring compounds, but anthropogenic activities introduce them in large quantities in different environmental compartments. This leads to the environment’s ability to foster life being reduced as human, animal, and plant health become threatened. This occurs due to bioaccumulation in the food chains as a result of the nondegradable state of the heavy metals. Remediation of heavy metals requires special attention to protect soil quality, air quality, water quality, human health, animal health, and all spheres as a collection. Developed physical and chemical heavy metal remediation technologies are demanding costs which are not feasible, time-consuming, and release additional waste to the environment. This chapter summarises the problems related to heavy metal pollution and various remediation technologies. A case study in South Africa mines were also used.",book:{id:"6534",slug:"heavy-metals",title:"Heavy Metals",fullTitle:"Heavy Metals"},signatures:"Vhahangwele Masindi and Khathutshelo L. Muedi",authors:[{id:"225304",title:"Dr.",name:"Vhahangwele",middleName:null,surname:"Masindi",slug:"vhahangwele-masindi",fullName:"Vhahangwele Masindi"},{id:"241403",title:"M.Sc.",name:"Khathutshelo",middleName:"Lilith",surname:"Muedi",slug:"khathutshelo-muedi",fullName:"Khathutshelo Muedi"}]},{id:"37067",doi:"10.5772/35482",title:"Fourier Transform Infrared Spectroscopy for Natural Fibres",slug:"fourier-transform-infrared-spectroscopy-for-natural-fibres",totalDownloads:9292,totalCrossrefCites:167,totalDimensionsCites:400,abstract:null,book:{id:"2270",slug:"fourier-transform-materials-analysis",title:"Fourier Transform",fullTitle:"Fourier Transform - Materials Analysis"},signatures:"Mizi Fan, Dasong Dai and Biao Huang",authors:[{id:"104647",title:"Prof.",name:"Mizi",middleName:null,surname:"Fan",slug:"mizi-fan",fullName:"Mizi Fan"}]},{id:"46243",doi:"10.5772/57255",title:"Corrosion Inhibitors – Principles, Mechanisms and Applications",slug:"corrosion-inhibitors-principles-mechanisms-and-applications",totalDownloads:13737,totalCrossrefCites:40,totalDimensionsCites:163,abstract:null,book:{id:"3817",slug:"developments-in-corrosion-protection",title:"Developments in Corrosion Protection",fullTitle:"Developments in Corrosion Protection"},signatures:"Camila G. Dariva and Alexandre F. Galio",authors:[{id:"169261",title:"Dr.",name:"Camila",middleName:"G.",surname:"Dariva",slug:"camila-dariva",fullName:"Camila Dariva"},{id:"170138",title:"Dr.",name:"Alexandre",middleName:"Ferreira",surname:"Galio",slug:"alexandre-galio",fullName:"Alexandre Galio"}]},{id:"44359",doi:"10.5772/56197",title:"Microstructure and Mechanical Properties of High Strength Two-Phase Titanium Alloys",slug:"microstructure-and-mechanical-properties-of-high-strength-two-phase-titanium-alloys",totalDownloads:10320,totalCrossrefCites:57,totalDimensionsCites:129,abstract:null,book:{id:"3494",slug:"titanium-alloys-advances-in-properties-control",title:"Titanium Alloys",fullTitle:"Titanium Alloys - Advances in Properties Control"},signatures:"J. Sieniawski, W. Ziaja, K. Kubiak and M. Motyka",authors:[{id:"101690",title:"Associate Prof.",name:"Maciej",middleName:null,surname:"Motyka",slug:"maciej-motyka",fullName:"Maciej Motyka"},{id:"109232",title:"Prof.",name:"Jan",middleName:null,surname:"Sieniawski",slug:"jan-sieniawski",fullName:"Jan Sieniawski"}]},{id:"46882",doi:"10.5772/58534",title:"Additive Manufacturing of Al Alloys and Aluminium Matrix Composites (AMCs)",slug:"additive-manufacturing-of-al-alloys-and-aluminium-matrix-composites-amcs-",totalDownloads:10131,totalCrossrefCites:52,totalDimensionsCites:117,abstract:null,book:{id:"3844",slug:"light-metal-alloys-applications",title:"Light Metal Alloys Applications",fullTitle:"Light Metal Alloys Applications"},signatures:"Diego Manfredi, Flaviana Calignano, Manickavasagam Krishnan,\nRiccardo Canali, Elisa Paola Ambrosio, Sara Biamino, Daniele Ugues,\nMatteo Pavese and Paolo Fino",authors:[{id:"16648",title:"Dr.",name:"Diego",middleName:null,surname:"Manfredi",slug:"diego-manfredi",fullName:"Diego Manfredi"},{id:"18978",title:"Dr.",name:"Matteo",middleName:null,surname:"Pavese",slug:"matteo-pavese",fullName:"Matteo Pavese"},{id:"19187",title:"Dr.",name:"Sara",middleName:null,surname:"Biamino",slug:"sara-biamino",fullName:"Sara Biamino"},{id:"19188",title:"Dr.",name:"Elisa",middleName:null,surname:"Ambrosio",slug:"elisa-ambrosio",fullName:"Elisa Ambrosio"},{id:"19189",title:"Dr.",name:"Paolo",middleName:null,surname:"Fino",slug:"paolo-fino",fullName:"Paolo Fino"},{id:"170227",title:"Dr.",name:"Flaviana",middleName:null,surname:"Calignano",slug:"flaviana-calignano",fullName:"Flaviana Calignano"},{id:"170228",title:"MSc.",name:"Riccardo",middleName:null,surname:"Canali",slug:"riccardo-canali",fullName:"Riccardo Canali"},{id:"170229",title:"MSc.",name:"Manickavasagam",middleName:null,surname:"Krishnan",slug:"manickavasagam-krishnan",fullName:"Manickavasagam Krishnan"}]}],mostDownloadedChaptersLast30Days:[{id:"70661",title:"Bioremediation Techniques for Polluted Environment: Concept, Advantages, Limitations, and Prospects",slug:"bioremediation-techniques-for-polluted-environment-concept-advantages-limitations-and-prospects",totalDownloads:2672,totalCrossrefCites:10,totalDimensionsCites:30,abstract:"Environmental pollution has been rising in the past few decades due to increased anthropogenic activities. Bioremediation is an attractive and successful cleaning technique to remove toxic waste from polluted environment. Bioremediation is highly involved in degradation, eradication, immobilization, or detoxification diverse chemical wastes and physical hazardous materials from the surrounding through the all-inclusive and action of microorganisms. The main principle is degrading and converting pollutants to less toxic forms. Bioremediation can be carried out ex-situ and in-situ, depending on several factors, which include but not limited to cost, site characteristics, type, and concentration of pollutants. Hence, appropriate bioremediation technique is selected. Additionally, the major methodologies to develop bioremediation are biostimulation, bioaugmentation, bioventing, biopiles, and bioattenuation provided the environmental factors that decide the completion of bioremediation. Bioremediation is the most effective, economical, eco-friendly management tool to manage the polluted environment. All bioremediation techniques have its own advantage and disadvantage because it has its own specific applications.",book:{id:"9343",slug:"trace-metals-in-the-environment-new-approaches-and-recent-advances",title:"Trace Metals in the Environment",fullTitle:"Trace Metals in the Environment - New Approaches and Recent Advances"},signatures:"Indu Sharma",authors:[{id:"301262",title:"Associate Prof.",name:"Indu",middleName:null,surname:"Sharma",slug:"indu-sharma",fullName:"Indu Sharma"}]},{id:"60680",title:"Environmental Contamination by Heavy Metals",slug:"environmental-contamination-by-heavy-metals",totalDownloads:16251,totalCrossrefCites:187,totalDimensionsCites:408,abstract:"The environment and its compartments have been severely polluted by heavy metals. This has compromised the ability of the environment to foster life and render its intrinsic values. Heavy metals are known to be naturally occurring compounds, but anthropogenic activities introduce them in large quantities in different environmental compartments. This leads to the environment’s ability to foster life being reduced as human, animal, and plant health become threatened. This occurs due to bioaccumulation in the food chains as a result of the nondegradable state of the heavy metals. Remediation of heavy metals requires special attention to protect soil quality, air quality, water quality, human health, animal health, and all spheres as a collection. Developed physical and chemical heavy metal remediation technologies are demanding costs which are not feasible, time-consuming, and release additional waste to the environment. This chapter summarises the problems related to heavy metal pollution and various remediation technologies. A case study in South Africa mines were also used.",book:{id:"6534",slug:"heavy-metals",title:"Heavy Metals",fullTitle:"Heavy Metals"},signatures:"Vhahangwele Masindi and Khathutshelo L. Muedi",authors:[{id:"225304",title:"Dr.",name:"Vhahangwele",middleName:null,surname:"Masindi",slug:"vhahangwele-masindi",fullName:"Vhahangwele Masindi"},{id:"241403",title:"M.Sc.",name:"Khathutshelo",middleName:"Lilith",surname:"Muedi",slug:"khathutshelo-muedi",fullName:"Khathutshelo Muedi"}]},{id:"59905",title:"Synthesis of Silver Nanoparticles",slug:"synthesis-of-silver-nanoparticles",totalDownloads:6894,totalCrossrefCites:9,totalDimensionsCites:19,abstract:"Nanoparticles of noble metals, especially the silver nanoparticles, have been widely used in different fields of science. Their unique properties, which can be incorporated into biosensor materials, composite fibers, cosmetic products, antimicrobial applications, conducting materials and electronic components, make them a very important subject to be studied by chemistry, biology, healthcare, electronic and other related branches. These unique properties depend upon size and shape of the silver nanoparticles. Different preparation methods have been reported for the synthesis of the silver nanoparticles, such as electron irradiation, laser ablation, chemical reduction, biological artificial methods, photochemical methods and microwave processing. This chapter aims to inform the synthesis methods of the silver nanoparticles.",book:{id:"6552",slug:"silver-nanoparticles-fabrication-characterization-and-applications",title:"Silver Nanoparticles",fullTitle:"Silver Nanoparticles - Fabrication, Characterization and Applications"},signatures:"Remziye Güzel and Gülbahar Erdal",authors:[{id:"226613",title:"Dr.",name:"Remziye",middleName:null,surname:"Güzel",slug:"remziye-guzel",fullName:"Remziye Güzel"},{id:"240772",title:"MSc.",name:"Gülbahar",middleName:null,surname:"Erdal",slug:"gulbahar-erdal",fullName:"Gülbahar Erdal"}]},{id:"71326",title:"Stability of Metal Complexes",slug:"stability-of-metal-complexes",totalDownloads:2384,totalCrossrefCites:7,totalDimensionsCites:11,abstract:"The stability of coordination complex is an important factor that decides the stability and reactivity of a metal complex. The stability of metal complex is governed by two different aspects such as thermodynamic and kinetic stabilities. The correlation between stability and reactivity of coordination compounds has been described in this chapter. This chapter also enlists the factors influencing the stability of metal complexes such as the nature of metal ions, ligands, bonding between metal ions and ligands, etc. In addition, the methods available for the determination of stability constants are given in detail.",book:{id:"9190",slug:"stability-and-applications-of-coordination-compounds",title:"Stability and Applications of Coordination Compounds",fullTitle:"Stability and Applications of Coordination Compounds"},signatures:"Senthilkumar Muthaiah, Anita Bhatia and Muthukumar Kannan",authors:null},{id:"60518",title:"Synthetic Methods for Titanium Dioxide Nanoparticles: A Review",slug:"synthetic-methods-for-titanium-dioxide-nanoparticles-a-review",totalDownloads:5268,totalCrossrefCites:29,totalDimensionsCites:55,abstract:"Titanium dioxide (TiO2) semiconductor nanoparticles are one kind of important and promising photocatalysts in photocatalysis because of their unique optical and electronic properties. Their properties, which are determined by the preparation method, are very crucial in photocatalysis. In this chapter, an overview was carried out on the different methods that are used or have been used to prepare titanium dioxide nanoparticles. There are various methods that can be used to synthesize TiO2 and the most commonly used methods include sol-gel process, chemical vapor deposition (CVD) and hydrothermal method among others. This review will focus on selected preparation methods of titanium dioxide photocatalyst.",book:{id:"6426",slug:"titanium-dioxide-material-for-a-sustainable-environment",title:"Titanium Dioxide",fullTitle:"Titanium Dioxide - Material for a Sustainable Environment"},signatures:"Pardon Nyamukamba, Omobola Okoh, Henry Mungondori,\nRaymond Taziwa and Simcelile Zinya",authors:[{id:"196100",title:"Dr.",name:"Raymond",middleName:null,surname:"Taziwa",slug:"raymond-taziwa",fullName:"Raymond Taziwa"},{id:"219920",title:"Prof.",name:"Omobola",middleName:null,surname:"Okoh",slug:"omobola-okoh",fullName:"Omobola Okoh"},{id:"226567",title:"Dr.",name:"Pardon",middleName:null,surname:"Nyamukamba",slug:"pardon-nyamukamba",fullName:"Pardon Nyamukamba"},{id:"239758",title:"Mr.",name:"Simcelile",middleName:null,surname:"Zinya",slug:"simcelile-zinya",fullName:"Simcelile Zinya"}]}],onlineFirstChaptersFilter:{topicId:"158",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82118",title:"Surface Hardening of Stainless Steel",slug:"surface-hardening-of-stainless-steel",totalDownloads:24,totalDimensionsCites:0,doi:"10.5772/intechopen.105036",abstract:"The addition of nitrogen to stainless steel improves mechanical and corrosion properties. Nitrogen-bearing stainless steel (HNSS) is a new corrosion-resistant alloy class exhibiting better tribological properties. High-pressure and powder metallurgy techniques were developed for the fabrication of HNSS. Solid-state routes allow nitrogen introduction through thermochemical, implantation, or plasma surface treatments. High-temperature gas nitriding (HTGN), carried out in an N2 atmosphere in the 1000°C range, allows N uptake, obtaining thick, ~0.5–1.0 wt.% N austenitic cases. HTGN is different from conventional nitriding, performed in the 500°C range, where intense CrxNy precipitation occurs, impairing the corrosion resistance. Low-temperature plasma nitriding (LTPN) introduces more N in solution, and colossal supersaturated expanded phases (~45 at.%N) are formed. N supersaturation and compressive stresses increase the hardness of the surface layer to 10–14 GPa. Ferritic, martensitic, duplex, and precipitation-hardened stainless steels can be surface-treated by LTPN, obtaining expanded ferrite and martensite. However, single LTPN stainless steel may prematurely fail when submitted to high loading, as the thin and hard expanded layers collapse due to lack of load-bearing capacity. Duplex-nitriding treatment (HTGN + LTPN) results in a thick nitrogen-rich hardened austenite substrate layer, granting mechanical support and adhesion to the expanded austenite layer.",book:{id:"11076",title:"Stainless Steels",coverURL:"https://cdn.intechopen.com/books/images_new/11076.jpg"},signatures:"André Paulo Tschiptschin and Carlos Eduardo Pinedo"},{id:"81579",title:"Welding Based Additive Manufacturing: Fundamentals",slug:"welding-based-additive-manufacturing-fundamentals",totalDownloads:32,totalDimensionsCites:0,doi:"10.5772/intechopen.104768",abstract:"Additive Manufacturing (AM) has drawn abundant attention over the past decades in the manufacturing and fabrication industries, especially to make part models and prototypes. This chapter introduces a potential welding based AM process called Wire Arc Additive Manufacturing (WAAM) for the fabrication of near-net shaped metal components including stainless steel components. To start with traditional AM processes, various fundamental traditional AM for the fabrication of components have been presented. Wire Arc Additive Manufacturing (WAAM) has been explained with its variants, synonyms, different welding processes to suit WAAM particularly to weld stainless steel metal; primary process selections for working with WAAM, important metals, and alloys that could be used in WAAM have been elaborated. A case study for WAAM fabrication of AISI 316 L stainless steel plate is included to introduce the fabrication of metal components using WAAM. Further, the most common defects which possibly play a vital role in WAAM components fabrication and a few of the future challenges regarding WAAM development are discussed. Fundamental information covered in this chapter could be more beneficial to beginners for the understanding of WAAM process generally including stainless steel component fabrication in a lucid tactic.",book:{id:"11076",title:"Stainless Steels",coverURL:"https://cdn.intechopen.com/books/images_new/11076.jpg"},signatures:"Maruthasalam Sowrirajan, Selvaraj Vijayan and Munusamy Arulraj"},{id:"80664",title:"Dependence of Corrosion Resistance of Austenitic Chromium-Nickel Steels on the Magnetic State of Austenite",slug:"dependence-of-corrosion-resistance-of-austenitic-chromium-nickel-steels-on-the-magnetic-state-of-aus",totalDownloads:59,totalDimensionsCites:0,doi:"10.5772/intechopen.102388",abstract:"Corrosive behavior of austenitic chromium-nickel steels from the magnetic state (parameter χ0) of austenite, pre-formed to interact with aggressive media are research. Correlation between the rate K of pitting corrosion and the specific magnetic susceptibility χ0 of austenite was experimentally established. It is experimentally established that the corrosion resistance of austenitic steels AISI304, 08Cr18Ni10, AISI 321, 08Cr18Ni10Тi (containing a low amount of δ-ferrite ∼0.005…0.5%) depends on the magnetic state of austenite: the corrosion rate of steel decreases with increases χ0 austenite. The tendency of change in the corrosion rate of austenitic alloy with a high nickel content 06Crh28NiMoCuTi (not contain δ-ferrite) has the opposite character: with increasing χ0, the corrosion rate of the alloy increases is revealed. For austenitic chromium-nickel steels, the corrosion rates of the individual (austenite (A), δ-ferrite (F), strain-induced α′-martensite (M)) and total (A + F, A + M and A + F + M) phases are determined. It is proposed to predict corrosion according to the specific magnetic susceptibility χ0 of austenite and the amount δ-ferrite.",book:{id:"11076",title:"Stainless Steels",coverURL:"https://cdn.intechopen.com/books/images_new/11076.jpg"},signatures:"Gennadii Snizhnoi"},{id:"80199",title:"The Evaluation of the Comparative Corrosion Behaviour of Conventional and Low-Nickel Austenitic Stainless Steel: Hercules™ Alloy",slug:"the-evaluation-of-the-comparative-corrosion-behaviour-of-conventional-and-low-nickel-austenitic-stai",totalDownloads:55,totalDimensionsCites:0,doi:"10.5772/intechopen.102381",abstract:"Austenitic stainless steels require approximately 8% Ni to maintain austenitic microstructure at room temperature for alloys such as 304 stainless steel (304SS). Ni contributes approximately 60% of the total material cost and its price fluctuates, making the cost of austenitic stainless steel unpredictable. The use of low-nickel austenitic stainless steels as a substitute has been considered in order to remedy costs associated with Ni price fluctuations. Alloying elements such as Mn and N have been considered, however they have been found to reduce corrosion resistance. A new alloy namely Hercules™ has been developed with reduced Ni content (1.8–2% Ni). This chapter presents a comparative study of the corrosion behavior of Hercules™ and 304SS in different solutions. The alloys were evaluated using cyclic polarisation technique and immersion tests. The results demonstrated that the corrosion resistance of Hercules™ is comparable to that of 304SS. This presents the alloys as potential industrial substitutes of each other.",book:{id:"11076",title:"Stainless Steels",coverURL:"https://cdn.intechopen.com/books/images_new/11076.jpg"},signatures:"Duduzile Nkomo and Nomsombuluko Masia"},{id:"80346",title:"Nitrogen Supersaturation of AISI316 Base Stainless Steels at 673 K and 623 K for Hardening and Microstructure Control",slug:"nitrogen-supersaturation-of-aisi316-base-stainless-steels-at-673-k-and-623-k-for-hardening-and-micro",totalDownloads:59,totalDimensionsCites:1,doi:"10.5772/intechopen.102387",abstract:"The high-density plasma nitriding at 673 K and 623 K was employed to make 10% of nitrogen supersaturation on AISI316 base austenitic stainless steels. The processing parameters and nitrogen-hydrogen gas flow ratio were optimized to increase the yield of N2+ ion and NH-radical for efficient nitriding. The nitrided AISI316 specimens were prepared for multidimensional analysis to describe the fundamental features of low-temperature plasma nitriding. First, macroscopic evaluation revealed that nitrogen supersaturation induced the γ-lattice expansion and the higher nitrogen content than 4% of mass in depth. The mesoscopic analysis describes the holding temperature and initial grain-size effects on the microstructure changes. Plastic straining, grain-size refinement, and nitrogen zone-boundary diffusion processes advance with nitrogen supersaturation to drive the inner nitriding behavior. The microscopic analysis explains the microstructure refinement, the two-phase structuring, and the microstructure modification. Through this multi-dimensional analysis, the essential characteristics of the low-temperature plasma nitriding of 316 austenitic stainless steels were precisely understood to extend the engineering treatise on the bulk nitrogen stainless steels for surface modification and treatment of stainless steels by nitriding. This plasma nitriding was applied to strengthen and harden the AISI316 wire surfaces toward its application on surgery wires.",book:{id:"11076",title:"Stainless Steels",coverURL:"https://cdn.intechopen.com/books/images_new/11076.jpg"},signatures:"Tatsuhiko Aizawa, Tomomi Shiratori, Tomoaki Yoshino, Yohei Suzuki and Takafumi Komatsu"},{id:"79904",title:"Corrosion Resistance, Evaluation Methods, and Surface Treatments of Stainless Steels",slug:"corrosion-resistance-evaluation-methods-and-surface-treatments-of-stainless-steels",totalDownloads:106,totalDimensionsCites:1,doi:"10.5772/intechopen.101430",abstract:"Stainless steels are widely recognized and find applications in many engineering industries and companies due to their excellent properties including high resistance to corrosion as a result of their minimum 10.5% chromium content, exceptional strength and durability, temperature resistance, high recyclability, and easy formability. In the present book chapter, the basic concepts of stainless steel including its applications, classifications, and corrosion properties will first be discussed. Thereafter, their corrosion behaviour will then be explained. 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We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. 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