Simulation conditions for DCHE.
\r\n\tThe book will aim to cover also the synthesis and optical properties of noble metal nanostructures, patterned surfaces, continuous or grated surfaces, and devices. This book intends to provide the reader with a comprehensive overview of the current state-of-the-art in plasmonic microscopy, surface-enhanced spectroscopic properties, such as Raman scattering or fluorescence, as well developments in techniques such as surface plasmon resonance and near-field scanning optical microscopy but also data transmission, plasmonic light modulators, and optoplasmonic networks.
",isbn:"978-1-80356-003-8",printIsbn:"978-1-80356-002-1",pdfIsbn:"978-1-80356-004-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"15d44e3a7898842276a9a9da9863a59d",bookSignature:"Dr. Patrick Steglich",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11147.jpg",keywords:"Biosensors, Surface Plasmon Resonance, Optoplasmonic Networks, Plasmonic Communication, Fabrication Methods, Device Simulation, Device Optimization, Surface Plasmon Resonance, Plasmonic Microscopy, Phonon-Plasmon Interaction, Physical Background, Mathematical Background",numberOfDownloads:24,numberOfWosCitations:0,numberOfCrossrefCitations:1,numberOfDimensionsCitations:1,numberOfTotalCitations:2,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 19th 2021",dateEndSecondStepPublish:"February 25th 2022",dateEndThirdStepPublish:"April 26th 2022",dateEndFourthStepPublish:"July 15th 2022",dateEndFifthStepPublish:"September 13th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"6 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"A senior researcher in photonics at IHP - Leibniz Institute for Innovations in Microelectronics, book author, lecturer at Technical Unversity of Applied Sciences Wildau, and holder of three registered patents.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"223128",title:"Dr.",name:"Patrick",middleName:null,surname:"Steglich",slug:"patrick-steglich",fullName:"Patrick Steglich",profilePictureURL:"https://mts.intechopen.com/storage/users/223128/images/system/223128.jpeg",biography:"Patrick Steglich is a research associate at the IHP - Leibniz-Institut für innovative Mikroelektronik, Germany, and lecturer for photonics and optical technologies at the Technical University of Applied Sciences Wildau, Germany. He obtained a master's degree in Photonics from the Technical University of Applied Sciences Wildau in 2013. In 2017, he received his PhD in Industrial Engineering from the Università degli Studi di Roma 'Tor Vergata” for his work in the field of integrated photonics for communication and sensing. 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In the retina there is a compromise between transparency and optimal oxygenation [3]. Thus, retinal vasculature must show special characteristics in order to minimize their interference with the light path. Retinal capillaries are sparse and small [4], representing only 5% of the total retinal mass [5]. Hence, retinal blood volume is relatively low [6]. This feature, together with an extremely active cellular metabolism, 10% of resting body energy expenditure is consumed by retinal tissue [7], makes retina very susceptible to hypoxia.
The study of retinal vasculature has an increasing relevance, since vascular alterations are one of the earliest events observed during retinopathy [8]. Vascular alterations compromise blood flow, diminish oxygen supply, and neovascularization develops in response to hypoxia. This neovascularization is the most common cause of blindness, with a growing social impact in the world [9].
The structure of the mouse retina has been extensively studied anatomically using silver impregnations [10], Nissl staining [11], electron microscopy [12, 13], differential interference contrast microscopy [14] and confocal laser microscopy [15]. More specifically, mouse retinal blood vessels have been analyzed by angiography using fluorescent dyes [16], vascular corrosion cast [17], trypsin/pepsin digestion [18] and confocal laser microscopy (CLM) [19-23]. However, retinal whole-mount observation by confocal laser technology is the only method that allows a three-dimensional microscopical analysis of the entire retina, combining the use of fluorescent markers for proteins, signaling molecules, etc.
The visual organ, the eye, is a structure that transforms light into electrical impulses, which are sent to the brain. The visual organ is formed by the eyeball and the accessory ocular organs. Lids, lacrimal glands and extraocular muscles provide protection and help to the visual function (Figure 1A).
The adult mouse is a very small nocturnal mammal with a relatively small eyeball having an axial length from anterior cornea to choroid of about 3.4 mm [24]. As is typical for nocturnal mammals, the mouse eyeball resembles a hollow sphere with a relatively large cornea. The eyeball is formed by three layers or tunicae, which contains the eye chambers and a very large lens that represents approximately 65% of the axial length (Figure 1B). The anterior chamber is placed between the cornea and the iris. The posterior chamber is the space situated between the iris and the lens. The vitreous chamber of the eyeball is placed behind the lens, surrounded by the retina (Figure 1B).The three tunicae of the eyeball are concentrically placed and, from most internal to most external, are: the nervous layer, formed by the retina; the vascular layer, where can be found choroid, ciliary body and iris; and the external layer, which is formed by cornea and sclera [25] (Figure 1B).
The retina is the most complex part of the eye. Its structure and function is similar to those of the cerebral cortex. In fact, retina can be considered as an outpouching of central nervous system during embryonic development. The retina comprises a blind part, insensitive to light, associated with the ciliary body and the iris; and an optical part, containing photoreceptors. In turn, optical part is formed by two sheets: the neuroepithelial stratum, composed by neurons, and the retinal pigmentary epithelium. Mouse neuroretina is composed by eight layers (Figure 1C): the nerve fiber layer, hardly distinguishable in equatorial retina; the ganglion cell layer; the inner plexiform layer; the inner nuclear layer, containing bipolar, amacrine, horizontal and the nuclei of Müller cells; the outer plexiform layer; the outer nuclear layer, formed by photoreceptors nuclei; and the layers of internal and external segments of photoreceptors. Two limiting membranes can also be distinguished: the internal limiting membrane, placed between the vitreous and the retina; and external limiting membrane, found between the outer nuclear layer and the external segment of photoreceptors [25].
Topography and structure of the retina in the mouse eye. Enucleated eye (A). Paraffin section of an eye stained with Azan trichrome (B). Hematoxylin/eosin stained paraffin section of retina (C). 1: cornea; 2: iris; 3: anterior chamber; 4: posterior chamber; 5: lens; 6: vitreous chamber; 7: retina; 8: choroid; 9: sclera; 10: optic nerve; 11: extraocular muscles; 12: inner plexiform layer; 13: outer plexiform layer; 14: photoreceptor inner segment; 15: photoreceptor outer segment; GCL: ganglion cell layer; INL: inner nuclear layer; ONL: outer nuclear layer; RPE: retinal pigmentary epithelium; arrow head: internal limiting membrane; arrow: external limiting membrane. Scale bar: 34 µm.
Mice and humans have holoangiotic retinas [26]. In these species the entire retina is vascularized, in contrast with anangiotic retinas, such as the avian retinas, where there are not blood vessels inside the retina. In holoangiotic retinas blood flow is directed from the optic disc radially to the periphery of the retina, and vasculature consists of arteries, veins and a wide network of capillaries (Figures 2A and 2B). The retinal circulation develops from the hyaloid artery that regresses after birth. Hyaloid blood vessels following a template of astrocytes growth superficially and deeply forming the retinal capillary plexi [27]. In mouse retina, as happens in most of the mammals including man, blood supply is carried out by two different vascular systems: retinal vessels, which irrigate from the internal limiting membrane to the inner nuclear layer; and choroidal vessels that supply the rest of the retina [25] (Figure 2F).
Blood vessel distribution in mouse retina. To show the topography of blood vessels in the mouse retina, scan laser ophtalmoscope images (A and C), collagen IV antibody immunohistochemistry (green) of whole-mount (B,D and E) and paraffin embedded (F) mouse retinas are presented. Nuclei counterstained with ToPro-3 (blue). A: arteriole; V: venule; OD: optic disc; GCL: ganglion cell layer; INL: inner nuclear layer; ONL: outer nuclear layer; arrowhead: blood vessels. Scale bars: 108 µm (D), 122 µm (E) and 34 µm (F).
The main source for retinal blood supply is the internal carotid artery that gives rise to the ophthalmic artery. This artery goes along with the optic nerve and internally is the origin of the central retinal artery [25, 28, 29]. At the level of the optic disc, the central retinal artery branch in four to eight retinal arterioles, depending on mouse strain. Arterioles run towards retinal periphery, where retinal venules are originated. (Figures 2C and 2D). Retinal arterioles are the origin of precapillary arterioles, which give rise to a capillary network settled between retinal arterioles and venules (Figure 2E). Capillaries are placed in the retina forming two plexi: internal vascular plexus, at the level of ganglion cells and inner plexiform layers; and external vascular plexus, between inner and outer nuclear layers (Figure 2F). The figure 3 shows a schematic representation of capillary retinal plexi (adapted from [19]). Retinal capillaries converge into retinal venules, which course parallel to arterioles and drive hypoxic blood to the central retinal vein (Figures 2C and 2D).
Representative schema of vascular architecture and blood flow in mouse retina. Blood flows from arterioles to venules through a capillary network. Capillaries are placed forming two plexi, the most superficial situated in the outer plexiform layer and the deepest localized in the ganglion cell and inner plexiform layers. Hypoxic blood is collected from capillaries by retinal venules. A: arteriole; V: venule; arrows: blood flow direction.
The structure of retinal blood vessels is similar to other localizations of the body. The blood vessel wall can be divided in three layers or tunicae: the adventitia layer, the most external, is formed by connective tissue; the media layer, where can be found smooth muscle cells; and the intima layer, consisting in a monolayer of endothelial cells [30]. Retinal arterioles show a tunica adventitia, mainly formed by collagen IV, surrounding all cellular components of blood vessel wall (Figure 4A). Retinal arterioles have a well-developed tunica media, formed by one layer of smooth muscle cells placed perpendicularly to vascular axis (Figures 4B and 4C). Smooth muscle cell number diminishes when arterioles branch in precapillary arterioles, forming a non-continuous layer of sparse smooth muscle cells. Finally, the tunica intima is made of endothelial cells placed parallel to the vessel axis (Figure 4D).
Morphology and composition of retinal arterioles. Different markers were used in order to show the components of the arteriole wall. (A) collagen IV antibody (green) was used to specifically stain basement membrane. Smooth muscle cells (red) were evidenced by means of α-smooth muscle actin antibody (B) and phalloidin (C). (D) Lectin from
Retinal capillaries are formed by pericytes and endothelial cells surrounded by basement membrane (Figure 5A). Pericytes are a contractile cell population positive in retina for β-actin (Figure 5B), nestin (Figure 5C), NG2 (Figure 5D) and PDGF-Rβ (Figure 5E), among others [31-34]. Endothelial cells are placed in the most internal part of capillaries, in direct contact with blood stream. These cells show an elongated morphology with a big nucleus that protrudes to the vascular lumen (Figure 5). Different markers stain specifically endothelial cells, among others: Von Willebrand factor (Figure 6A), PECAM-1 (Figure 6B) and CD34 (Figure 6C). As happens in the brain, endothelial cells are connected by tight junctions (
Retinal venules show, as arterioles do, three concentrically placed layers: a tunica adventitia mainly formed of collagen IV (Figure 7A); a tunica media, consisting of a non-continuous layer of sparse smooth muscle cells (Figure 7B); and a monolayer of endothelial cells, the tunica intima (Figure 7C).
Morphology and composition of retinal capillaries. (A, D and E) Blood basement membrane was marked with anti-collagen IV antibody (green). (B) Capillary morphology in a β-actin/EGFP (green) transgenic mouse. Note that pericytes expressed more β-actin than endothelial cells. (C) Pericytes expressed nestin in the retinal capillaries of a nestin/EGFP (green) transgenic mouse. Specific pericyte markers, such as NG2 (D) and PDGF-Rβ (E), has also been employed to show the distribution and morphology of pericytes. Nuclei counterstained with ToPro-3 (blue). E: endothelial cell; P: pericyte. Scale bars: 9.5 µm (A and B) and 7.3 µm (C,D and E).
Morphology and composition of retinal capillaries. (B,C and D) Blood basement membrane was marked with anti-collagen IV antibody (green). Endothelial cells were specifically marked with anti-Von Willebrand factor (red) (A), anti-PECAM-1 (red) (B) and anti-CD34 (red) (C) antibodies. (D) Endothelial cell contribution to blood retinal barrier was evidenced using anti-ocludin antibody, a specific marker for endothelial tight junctions. Nuclei counterstained with ToPro-3 (blue). Scale bars: 5.5 µm (A,B and C) and 6.8 µm (D).
Morphology and composition of retinal venules. Different markers were used in order to show the components of the venule wall. (A) Collagen IV antibody (green) was used to specifically stain basement membrane. (B) Smooth muscle cells were evidenced by phalloidin binding (red). (C) Immunohistochemistry against Von Willebrand factor (red) showed endothelial cell morphology. (D) Lectin from
In addition to neurons, retinal blood vessels are surrounded by glia that seems to play a role in the formation of blood-retinal barrier [5, 35-38] and the control of retinal blood flow [38]. The term glia encloses two components: neuroglia and microglia. Retinal neuroglia is formed by astrocytes and Müller cells (Figure 8). Astrocytes are only placed in the internal part of the retina, nerve fiber and ganglion cell layers, in close relation with arterioles and venules [39] (Figures 8A and 8B). The principal markers for astrocytes are glial fibrillary acidic protein (GFAP) (Figure 8A) and desmin (Figure 8B). The nuclei of Müller cells are localized in the inner nuclear layer and their cytoplasmic prolongations extend practically to the entire retina forming the inner and outer limiting membranes (Figure 1C). Müller cells are very easily distinguished using the PDGF-Rα (Figure 8C). Cytoplasmic prolongations of neuroglia, called vascular end-feet, contact with retinal blood vessels (Figures 8A, 8B and 8C).
Perivascular glia in mouse retina. Different cell markers were used in order to show perivascular neuroglia and microglia. GFAP (A) and desmin (B) mark astrocytes (arrow), PDGF-Rα (C) stain Müller cells (arrow) and Iba1 (D) is expressed by microglial cells (arrow). (A,B,C and D) Blood basement membrane was marked with anti-collagen IV antibody (green). Nuclei counterstained with ToPro-3 (blue). Arrowhead: vascular end-foot. Scale bars: 23 µm (A,B and C) and 20 µm (D).
Retinal microglia originates from hemopoietic cells and invade the retina from the blood vessels of the ciliary body, iris and retinal vasculature [40]. Resting microglial cells are scattered troughout the retina forming a network of potential immunoephector cells, easily marked with Iba1 (Figure 8D). Several studies show that microglial cells have characteristics of dendritic antigen-presenting cells, while others resemble macrophages [41]. During retinopathy, activated microglial cells participate in phagocytosis of debris and facilitate the regenerative processes. Microglial cells are also in contact with blood vessels, forming a special subtype of perivascular microglial cells, localized in the perivascular space of Virchow-Robin (Figures 4D and 7D).
During the examination of retinal vasculature labeled with two different fluorescent markers, emission signals can often overlap in the final image. This effect, known as colocalization, occurs when fluorescent dyes bind to molecules residing in a very close spatial position in the tissue [42]. Although colocalization is getting more relevance in modern cell and molecular biological studies, it is probably one of the most misrepresented and misunderstood phenomena. In this way, proteins continue to be described as more or less colocalized with no quantitative justification. This lack of information prevents researchers to analyze protein dynamics or protein-protein interactions [43].
In Figure 9 we can observe a retinal arteriole with the blood vessel basement membrane stained with anti-collagen IV (green) and anti-matrix metalloproteinase 2 (MMP2) (red). MMP2 is a constitutive gelatinase protein that can be observed in a wide variety of healthy mice tissues [44]. One of the main substrates of MMP2 is collagen IV, so MMP2 colocalize with collagen IV in retinal arterioles (yellow). An accurate colocalization analysis is only possible if fluorescent emission spectra are well separated between fluorophores and a correct filter setting is used (Figure 9). When a high degree of emission spectra overlap and/or filter combinations are not well defined the resulting colocalization will be meaningless [42].
Colocalization of collagen IV with MMP2 in the arteriolar basement membrane. Double inmunohistochemistry was performed in retinal paraffin sections using antibodies against collagen IV (green) and matrix MMP2 (red). (A) Digital images of green (left image) and red (central image) channels showing colocalization (arrowheads) in the right image. Nuclei counterstained with ToPro-3 (blue). (B) Graphic representation in a scatterplot, where pure red and green pixels are between abscissa/ordinate and white lines. Colocalizating pixels are found inside white elliptic region. (C) Pearson’s correlation coefficient, where S1 and S2 are pixel intensities in channels red and green respectively; and S1aver (S2aver) is the average value of pixels in the first (second) channel. (D) Overlap coefficient, with k1 being sensitive to the differences in the intensities of channel 2 and k2 depending on the intensity of channel 1 pixels. Scale bar: 10.2 µm.
Graphical display for colocalization analysis is well represented by a fluorogram: a scatterplot which graphs the intensity of one color versus another on a two-dimensional histogram (Figure 9B). Along the y-axis is plotted green channel, while red channel is graphed on the x-axis. Thus, having each pixel a pair of fluorescent intensities in a Cartesian system. In the scatterplot, pixels having lower fluorescent intensities are close to the origin of abscissa and ordinate, while brighter pixels are dispersed along the graph (Figure 9B). Pure green and red pixels cluster close to the axes of the graph, while colocalized pixels are localized in the center and in the upper right hand of scatterplot (Figure 9B).
As discussed above, a quantitative assessment of colocalization is important in order to analyze protein dynamics and association. Using the information given by the scatterplot several values can be generated. Pearson’s correlation coefficient (Rr) is used as standard technique for image pattern recognition. This coefficient is employed to describe the degree of overlap between two images and can be calculated according to the equation seen in Figure 9 C. Values of this coefficient ranges from -1 to 1. The value -1 correspond to a complete lack of overlap between images and 1 a total match of pixels in the two images. Pearson’s coefficient takes into account only similarity among pixels in the two images, and does not consider information of pixel intensities. Thus, Pearson’s correlation coefficient can overestimate colocalization when the degree of colocalization is low [45].
Another standard value used to quantify colocalization is the overlap coefficient (R2) (Figure 9D). This coefficient uses two values (k1 and k2) in order to characterize colocalization in both channels. This coefficient avoids negative values, which have a harder interpretation. Some authors find this coefficient less reliable than Pearson’s correlation coefficient, since overlap coefficient is only applicable in images with similar intensities in the two channels [45].
Diabetic retinopathy is a common and specific microvascular complication of diabetes, and remains the leading cause of blindness in working-aged people [46]. Recent metadata studies established that in the world there are 93 million people with diabetic retinopathy [47]. Nearly all individuals with type 1 diabetes and more than 60% of individuals with type 2 diabetes have some degree of retinopathy after 20 years of disease. There are two phases in diabetic retinopathy. Early phase is known as non-proliferative diabetic retinopathy, and is characterized by thickening of capillary basement membrane, pericyte and vascular smooth muscle cell loss, capillary occlusion and formation of microaneurysms [48]. Proliferative retinopathy, the second phase of the disease, is characterized by the formation of new vessels that pas through the inner limiting retinal membrane and penetrate in the vitreous chamber. New vessels are surrounded by fibrous tissue that may contract, leading to retinal detachment and sudden visual loss. Neovascularization is a consequence of retinal increase of cytokines and growth factors produced in ischemic conditions. Proliferative retinopathy appears in approximately 50% of patients with type 1 diabetes and in about 15% of patients with type 2 diabetes [49].
Confocal laser microscopy allows the study of retinal blood vessels in diabetic mouse models (Fig. 10). Non-obese diabetic (NOD) mice develop type 1 diabetes by autoimmune destruction of pancreatic β cells [50].
Venule basement membrane alterations in diabetic retinopathy mouse models. Compare the morphology of basement membrane (green) between wild type (WT) mice and Non-obese diabetic (NOD) mice, and db/db mice. Nuclei counterstained with ToPro3 (blue). Blood basement membrane was marked with anti-collagen IV antibody. A: arteriole, V: venule. Scale bars: 21,2 µm (WT), 15 µm (NOD) and 16,2 µm (db/db).
The analysis of 8 months-old NOD mice whole-mount flat retinas marked with anti-collagen IV antibody showed basement membrane alterations in venules (Fig. 10). Similarly, db/db mice also showed alterations in basement membrane of retinal venules (Fig. 10). Db/db mice are homozygous for a mutation in the leptin receptor, and spontaneously develop type 2 diabetes [51]. These result suggested that, both in diabetes type 1 and 2, normal functions of blood basement membrane are altered in venules during diabetes. Venule basement membrane separate endothelial cells and smooth muscle cells from underlying connective tissue providing structural support, a selective barrier of filtration, and a substrate for molecular adhesion that modulates cells of the vascular wall.
By the above, we can conclude that the analysis of whole-mount retinas by laser confocal technology is a reliable method that allows a complete three-dimensional microscopical analysis of retinal blood vessels in health and disease. Combining the use of fluorescent markers for proteins, signaling molecules, etc, it will be possible study the topography and the structure of blood vessels. Thus, the use of confocal laser microscopy together with new mouse eye disease models may provide basis to fully understand the alterations of retinal vasculature during retinopathies.
We thank V. Melgarejo, L. Noya and A. Vazquez for technical assistance.
This work was supported by grants PS09/01152 from the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación, Spain and PTDC/SAU-ORG/110856/2009 from the Fundação para a Ciência e a Tecnologia do Ministério da Ciência, Tecnologia e Ensino Superior, Portugal.
The growing population and rising energy demand in many countries are imposing significant challenges to energy and environmental sustainability. The global energy and environmental scenarios are closely interlinked. That is, the problems of supply and use of energy are related to global warming and climate change [1]. The electrical energy consumption for air conditioning accounts for 20–40% of the total electricity used in buildings around the world today [2, 3]. Global energy demand for air conditioning is expected to triple by 2050, requiring new electricity capacity that is the equivalent to the combined electricity capacity of the United States, the EU, and Japan today. Accordingly, the global stock of air conditioners in buildings will grow to 5.6 billion by 2050, up from 1.6 billion today, which means new air conditioners are sold every second for the next 30 years [4].
In conventional air-conditioning systems, the compressor efficiency has shown a significant improvement chronologically from 1.2 kW/RT in the 1990s to 0.85 ± 0.05 kW/RT [5]. Since 2000, however, this improvement trend has leveled asymptotically, implying that efficiency improvement for compressor stages and heat exchangers has been saturated. Despite massive investment in the R&D of cooling technologies by chillers’ manufacturers, there are physical and material limits to which efficiency improvement of the major components in the cooling cycle can be attained. If one were to continue to improve the cooling efficiency of conventional chillers (with HFC refrigerants), the improvement in kW/RT may improve only marginally by less than 5% and no quantum efficiency improvement can be expected. Therefore, we believe that there is a need for an out-of-box solution for cooling where the consumption of energy and water can be reduced significantly, by as much as 50% so as to attain sustainable cooling for the future, addressing the space cooling needs of the present and future generations.
Recently, many studies have been focused on the development of a desiccant-coated heat exchanger (DCHE) to improve dehumidification performance by employing various desiccant materials and heat exchangers. Sunhor et al. [6] investigated experimentally the dehumidification behavior of AlPO-zeolite-coated heat exchanger by varying operating conditions. Chai et al. [7] presented a desiccant heat pump by combining a heat pump and a desiccant-coated heat exchanger. They employed a commercial silica gel to fabricate the desiccant-coated heat exchanger. Liang et al. [8] developed a desiccant-coated heat exchanger by using a microchannel heat exchanger and explored the performance under various operating conditions.
Many investigations also have been carried out mathematically and experimentally to study the effectiveness and COP of various dew-point evaporative coolers (DECs). Xu et al. [9] proposed a novel complex heat and mass exchanger (HMX) to improve the performance of a dew-point evaporative cooler. Duan et al. [10] explored the operational performance of a regenerative evaporative cooler under various conditions by analyzing the wet-bulb effectiveness and COP. Balyani et al. [11] conducted an economic, environmental, and thermodynamic analysis for the best cooling technology for various climates. Duan et al. [10] investigated experimentally the energy-saving potential of a prototype of a counter-flow regenerative cooler when it applied to China’s various regions. Jafarian et al. [12] developed GMDH-type neural networks for modeling and optimization of a flat plate dew-point counter flow IEC. Their results indicated that the COP was improved by 36.6% in hot and dry climate. Sohani et al. [13] conducted a multiobjective optimization to find the best design for two different dew-point evaporative coolers (i.e., counter-flow and cross-flow configurations).
In this study, firstly, we present an innovative decoupling cooling technology where latent cooling load and sensible cooling load are handled separately by a desiccant-coated heat exchanger (DCHE)-based dehumidifier and a dew-point evaporative cooler (DEC). Secondly, the performance is investigated numerically by analyzing the heat and mass transfer for both DCHE and the DEC.
In order to achieve a large reduction in the kW/RT of cooling, we propose a de-coupling cooling system that thermodynamically hybridizes a desiccant coated heat exchanger (DCHE)-based dehumidifier with a dew-point evaporative cooler (DEC). The decoupling cooling system handles a sensible cooling load and a latent cooling load separately. Figure 1 shows schematically the working principle of the decoupling system and its thermodynamic process on a psychrometric chart. The DCHE first removes the undesired moisture of humid outdoor air by the adsorption process. During this process, the desiccant adsorbs water vapor up to its equilibrium state, which is determined by the water vapor partial pressure and the temperature of the desiccant. At this point, the air temperature rises slightly due to adsorption heat generation. Cooling water (∼25°C) is supplied to remove the adsorption heat. Subsequently, the DEC cools down the dehumidified air up to the desired temperature, maintaining moisture level. The DEC consists of two different channels, namely a dry air channel and a wet-air channel. The dehumidified air coming out from the DCHE enters the dry air channel and releases the heat to the adjacent wet channel where a certain portion (20–35%) of the dehumidified air flows. The inner surface of the wet channel is always wetted by water film, and the evaporation takes place on the surface of the water film by absorbing the heat from itself, which causes the reduction of the temperature. It is noteworthy that the proposed system can handle all climate conditions successfully as well as it employs only waste heat for the regeneration of adsorbent, and the IEC uses only water as a refrigerant.
The working principle of the decoupling cooling system and its thermodynamic process on a psychrometric chart [
The desiccant-coated heat exchanger (DCHE) has been tested successfully for dehumidification application using only the low-temperature waste heat, as proven in the many case studies [2, 5, 15, 16, 17]. It could remove effectively moisture from humid air at a relative humidity ratio of 95% or a humidity ratio of above 20 g/kg using commercial adsorbents such as the silica gel, Zeolite, and MOF.
The DCHE was first introduced in order to improve the efficiency of a conventional solid desiccant dehumidifier, where solid desiccant was packed within a finned tube heat exchanger using a wire mesh. It was found that the packing method is inefficient due to poor heat transfer between solid desiccant and cooling water flowing through the heat exchanger. To coat the fins of the heat exchanger with the desiccant powder (ex. silica gel), hydroxyethylcellulose (HEC) is used as a binder since it exhibits the best binding force between the silica gel powder and the metal fins as well as it does not affect the adsorbate uptake capacity. During the dip-coating process, the heat exchanger is mounted onto a rotation machine to ensure a uniform coating layer. After that, the heat exchanger is placed into an oven for curing at 120°C for 12 h. The optimal coating thickness of the desiccant layer is found to be 0.1 mm in many studies [2, 14, 17, 18, 19, 20]. As depicted in Figure 2, the DCHE operates alternatively between two different modes, namely dehumidification (adsorption) mode and regeneration (desorption) mode. During the dehumidification mode, cooling water is supplied through the tubes in order to remove the adsorption heat, keeping the desiccant layer at a lower temperature and thus ensuring the highest uptake. On the other hand, hot water is supplied to release the water vapor that was adsorbed in the previous process during the regeneration mode. In this mode, each process continues until the uptake reaches the equilibrium uptake given the temperature and vapor pressure.
The concept of the desiccant-coated heat exchanger (DCHE) for the dehumidification: (a) adsorption/dehumidification process and (b) desorption/regeneration process.
As one of evaporative cooling methods, the dew-point evaporative cooler (DEC) is an energy-efficient and environmentally friendly sensible cooling device that employs only water as a refrigerant [21, 22, 23, 24]. The key feature of the DEC is that it employs the process air as the working air by purging the process air at a specified rate (20–30%) to reduce the supply air temperature below the wet-bulb temperature. The working principles for three different evaporative coolers are represented in Figure 3.
The working principles of evaporative cooling devices: (a) direct evaporative cooler, (b) indirect evaporative cooler, and (c) dew-point evaporative cooler.
The DEC differs from the conventional adiabatic cooling or commonly known as the cooling towers, swarm coolers, etc., where the air stream experience changes in both the temperature and absolute humidity. In the DEC, however, the product air (conditioned air for space cooling) is flowing in a dry channel while a small fraction (20–30%) of the product air is purged into a wet channel, where the purged air picks up the water vapor from a hydrophilic membrane that physically separates the evaporative moist air flowing in a counter-flow direction from the product air. The evaporative cooling in the wet channel cools the air flowing in the adjacent dry channel by heat transfer, and the processes are repeated in succession to achieve an overall cooling of primary air. At the end of a section, the saturated secondary air is purged while a part of the conditioned primary air (but at the original inlet humidity) is directed into the wet channel to repeat the evaporative cooling. Thermodynamically, it incurs two major energy losses to achieve cooling of the primary air stream: Firstly, energy is transferred to the air by a fan for maintaining the necessary air flows in all channels. Secondly, it continuously purges a fraction of the conditioned air of the non-wetted channel so as to reinitiate the evaporative cooling in successive wetted channels. The key feature here is that it continuously purges a fraction of the conditioned air so that the wet-bulb temperature of the conditioned stream can be lowered. The novelty of the decoupling cycle is the harnessing of evaporative potential of inlet air as well as waste heat, but the overall electricity incurred is a quantum lower than that of conventional chillers.
In order to investigate the heat and mass transfer for the DEC, a mathematical model was established. The differential control volume includes half the height of the dry and wet channels, the separating plate, and the water film. To simplify the heat and mass transfer processes, the following assumptions were made:
The air streams in both the dry and wet channels are at a steady state.
Air flow is laminar and fully developed in both channels.
The heat and mass transfer coefficients are constant.
Heat losses to the surroundings are negligible.
The water is continuously supplied to maintain the water film.
Mass transfer between the adsorbent (desiccant) and adsorbate (water vapor) can be expressed by LDF (linear driving force) model as follows:
where
where
Operation parameters | Baseline | Values |
---|---|---|
Process air inlet dry-bulb temperature [°C] | 35 | 27, 30, 40, 46 |
Process air inlet wet-bulb temperature [°C] | 24 | 19, 22, 24, 26 |
Process/regeneration air mass flow rate [kg/h] | 13 | |
Cooling water inlet temperature [°C] | 30 | 25, 30, 35 |
Hot water inlet temperature [°C] | 80 | 50, 60, 70, 80 |
Cycle time [sec.] | 150 |
Simulation conditions for DCHE.
where
The energy conservation equation of DCHE during adsorption/desorption process can be expressed by the following equation:
The first term on the left-hand side represents the rate of the change of energy content of the desiccant. The first term on the right-hand side represents the rate of adsorption heat generation. The second and the third terms are the rates of energy of air and water streams, respectively. Mass conservation equation of adsorbed phase of water vapor in the desiccant can be expressed by the following equation:
The first term on the left-hand side represents the rate of the change of moisture content in the desiccant.
where
The temperature of the process air at the outlet of DCHE is obtained by the following equation:
In order to develop the temperature and velocity profile along the channel, Nusselt number is approximated by the following equation [25].
The convective heat transfer coefficient for air side is calculated by Dittus-Boelter correlation:
The same correlation can be applied to predict the convective heat transfer coefficient for water side of the DCHE
The cooling/heat water flow inside the tube at a mass flow rate of 4 LPM and thus turbulent flow is formed with Reynolds number greater than 2300. Therefore, the Nusslet number is calculated from an empirical relation,
The exponent n equals to 0.4 for heating the water bulk flow (regeneration) and 0.33 for cooling conditions (dehumidification).
The mathematical model was for a differential control volume including half the height of the dry and wet channels, the separating plate, and the water film. To simplify the heat and mass transfer processes, the following assumptions were made:
The air streams in both the dry and wet channels are at steady state.
The air streams are fully developed laminar flow in both channels.
The heat and mass transfer coefficients are constant.
Heat losses to the surroundings are negligible.
The water is continuously supplied to maintain the water film.
In the dry channel of DEC, only a sensible cooling effect takes place by forced convective heat transfer without changing the humidity ratio. The energy conservation equation for air flowing in the dry channel is expressed by
where
Unlike the dry channel, both sensible and latent cooling effects take place in the wet channel. That is, both heat and mass transfer mechanism are involved to create cooling effect between the air and water film layer, and it is expressed as follows:
The first term on the left-hand side represents conduction heat transfer in the longitudinal direction, and the second term denotes advection heat transfer. The first term on the right-hand side refers to convective heat transfer, and the second term is heat transfer due to mass transfer between the air and the water film surface.
The mass transfer takes place only in the wet channel by the driving force of vapor partial pressure difference, and the mass conservation equation is written as
On the other hand, the energy balances for the water film and the separating plate are given as
Given the geometry of the channels and the operating conditions, the air streams are laminar flow with the maximum Reynolds number of 1048. The convective heat transfer coefficients for the dry and wet channels are expressed by Eq. (21).
The Nusselt number can be calculated by the correlation by Eq. (8) for fully developed laminar flow in the rectangular dry channel [26].
where AR is the aspect ratio of the channel (i.e., the ratio of the minimum to the maximum dimensions). The Nusselt number in the wet channel, in which both heat and mass transfer processes are involved, can be calculated by the following Eq. (25).
where
The convective mass transfer coefficient for the wet channel is approximated using heat and mass transfer analogy and can be expressed by Lewis number (Table 2).
Parameter | Values | |
---|---|---|
DCHE | Height of heat exchanger (mm) | 100 |
Length of heat exchanger (mm) | 25 | |
Width of heat exchanger (mm) | 100 | |
Pitch of fins (mm) | 1.8 | |
Thickness of the coating layer (mm) | 0.1 | |
DEC | Channel length (m) | 1.2 |
Channel width (m) | 0.3 | |
Channel height (mm) | 4 | |
Thickness of the separating plate(mm) | 0.1 | |
Thickness of the water film(mm) | 0.4 |
Design parameters of DCHE and DEC for numerical study.
The heat and mass transfer analysis for the decoupled cooling system has been carried out using the developed mathematical models. Variable-order method (ode15s) was employed for the transient simulation of DCHE and fourth-order method (bvp4c) for the steady-state simulation of DEC in MATLAB platform.
At first, simulation has been carried out for DCHE and examined the output states of the process air, namely the dry-bulb temperature and humidity ratio. The cyclic average values are then calculated and applied to the inlet conditions for DEC simulation. Simulation conditions were determined on the basis of IISO 5151:2017, which defines outdoor temperatures for three different climates, namely moderate climate, cool climates, and hot climates [27]. As shown in Table 1, moderate climate conditions (Tdb = 35°C Twb = 24°C) were considered as the baseline, and the cool climates are adopted for the lower limit condition and hot climates for the upper limit conditions.
The performance of DCHE was examined by analyzing two different metrics, namely moisture removal capacity (MRC) and latent cooling capacity (QL) and coefficient of performance (COPth). MRC is defined as the total amount of water vapor that is absorbed by the desiccant during the dehumidification process, and QL is defined as the cooling power to remove the moisture. They can be calculated by the following equation:
COPth is defined as the ratio of MRC to the total amount heat that is consumed by the desiccant during the regeneration process and can be calculated by the following equation:
For the evaluation of DEC, the temperature of product air (
The developed numerical models for DCHE and DEC have been validated with our experimental results and the published experimental results from references [28]. To evaluate the accuracy of mathematical models, relative error percentage and root-mean-square error (RMSE) were used for the consistency between simulated results and experimental data, and they are expressed as [29]
Figure 4 shows the comparison of the transient humidity variation with time during one cycle between the simulation results and the experimental results under the specific inlet air conditions of 32.6°C temperature with 14.0 gv/kgda humidity ratio. As shown in Figure 4a, the outlet humidity ratio obtained from the numerical analysis is quite similar to the experimental result. It is also observed from Figure 4b that the highest relative error is 8.6% with an RMSE of 0.0261.
Validation of the developed the mathematical model for DCHE: (a) transient outlet humidity ratio, (b) fitting of outlet humidity ratios in numerical and experimental results.
Figure 5 shows the comparison of the results obtained from the numerical analysis with the experimental results obtained from reference [23]. It also shows a good agreement between the simulation and experimental data under different inlet temperatures. The maximum deviation of 3.2% is observed with an RMSE of 0.0159.
Validation of the developed the mathematical model for DEC with experimental data from reference [
The performance of the DCHE was numerically analyzed, and simulation results are judiciously compared through a series of runs under various operating conditions. The inlet air temperature is 35°C for both dehumidification and regeneration processes. The temperatures of cooling and hot water are set at 30°C and 50°C, respectively. During the entire process, the inlet humidity ratio is kept to 14 g/kg. Figure 6 shows the dynamic variation of the performance of DCHE during three cycles under the baseline conditions where. It is easily seen from Figure 6a that the equilibrium uptake reaches its saturated state at 0.33 during the dehumidification process and at 0.12 during the regeneration process in a given condition. It is also seen that the actual uptake follows the trajectory of the equilibrium uptake at a different rate. It can also be observed from Figure 6b that the outlet humidity ratio reaches its equilibrium state at a faster rate during the regeneration process. However, it is noteworthy that the amount of the vapor that is released and adsorbed by the desiccant is conserved during the cycle so that the system continues to operate at a cyclic steady state. The lowest humidity ratio of 0.0092 kgv/kgda was observed at 16 s after the dehumidification process starts, and then it increases gradually up to its saturated level. It is also worthy to note that the desorption rate is a function of the adsorbent’s surface temperature. Therefore, as the surface temperature rises when the supply of hot water flows through the heat exchanger, the more adsorbates (water vapor) are released at faster rate. Hence, the regeneration time becomes shorter at higher surface temperature. This explains the reason for supplying the cooling water to the heat exchanger to enable a cooler adsorbent that is capable of adsorbing more moisture than the desorbing process.
Dynamic variations of the performance of the DCHE under the baseline conditions (Tdb = 35°C, Twb = 24°C,
Figure 7 shows the effect of change in the dry-bulb temperature of the process air on the performance of DCHE, namely moisture removal capacity (MRC), COPth, latent cooling capacity (Ql), and regeneration energy (Qreg). The dry-bulb temperature varied from 27 to 46°C while the other parameters were kept constant as in the baseline conditions. It is observed that MRC, Ql, and Qreg are markedly affected by process air inlet dry-bulb temperature, whereas COPth remains constantly around 0.125. MRC drops by 23.6% from 1.867 (Tdb = 35°C) to 1.42 (Tdb = 46°C). This is mainly because the humidity ratio decreases from 0.0142 to 0.0096, which implies the air carries less water vapor at a higher temperature when wet-bulb temperate is fixed at constant.
Effect of change in the process air dry-bulb temperature on the performance of DCHE: (a) moisture removal capacity (MRC) and COPth; (b) latent cooling capacity (Ql) and regeneration energy (Qreg).
Figure 8 shows the effect of change in the wet-bulb temperature of the process air on the performance of DCHE. Unlike the previous results, the performance indices are raised when increasing the wet-bulb temperature except for COPth. The reason for this is that the water vapor partial pressure increases as the dry-bulb temperature rises when the wet-blub temperature is fixed. Hence, the equilibrium uptake of the desiccant increases, which is defined as the ratio of the vapor partial pressure to the saturation pressure at the desiccant temperature. As a result, more water vapors can be absorbed by the desiccant. The highest values for MRC, Ql and Qreg were 1.87 g/cycle and 30.1 W, respectively when Twb = 24°C. It can be observed that MRC, Ql, and Qreg decrease slightly at Twb = 26°C.
Effect of change in the process air wet-bulb temperature on the performance of DCHE: (a) moisture removal capacity (MRC) and COPth; (b) latent cooling capacity (Ql) and regeneration energy (Qreg).
Figure 9 depicts the temperature and humidity ratio profiles of the product air and the working air for DEC under a specified operating condition. The product air flows along the channel length and the working air flow reversely in a counter-flow manner. It is observed from Figure 9b that the product air is purged at the end of the channel and is flowed back to the inlet. The product air temperature drops sensibly from 26°C to 16.18°C while the working air temperature increases from 16.8 to 24°C. It can be found that the temperature drops significantly within a distance of 0.1 m from the end of the channel immediately after being purged. This is because that instantaneous evaporation takes place from the surface of the water film by the continuously purged air. As depicted n Figure 9b, the relative humidity surges rapidly from 0.4 to 0.98 in the immediate vicinity of the exit. In addition, once the actual humidity ratio reaches the saturation level, both humidity ratios increase gradually, which allows for water to evaporate continuously along the channel length.
Temperature profile (a) and humidity ratio profile (b) of each component in DEC.
Figure 10 depicts the effect of change in the air temperature and humidity ratio on the performance of DEC while other parameters are kept constant as in the baseline conditions. The dry-bulb temperature varies from 26 to 38°C, and the humidity ratio varies from 0.005 kgv/kgda to 0.0187 kgv/kgda respectively. It can be found that the performance indices are almost linearly dependent on the air dry-bulb temperature and the humidity ratio. For the dew-point effectiveness, it varies between 0.44 and 0.57 when the dry-bulb temperature varies from 26 to 38°C. It can be inferred that the higher the air temperature is, the higher the dew-point effectiveness. It should be noted that the higher dew-point effectiveness and the wet-bulb effectiveness can be expected at a higher humidity ratio, whereas higher cooling capacity can be obtained at a lower humidity ratio. This is because both wet-bulb and dew-point temperatures increase in proportion to the humidity ratio when the dry-bulb temperature is fixed. Therefore, the potential for water evaporation is declined, and thus, the temperature drop in the dry channel gets closer to its maximum (i.e., 1).
Effect of change in the air temperature and humidity ratio on the performance of DEC: (a) dew-point effectiveness, (b) wet-bulb effectiveness, and (c) cooling capacity.
This study presents an innovative decoupling cooling technology where latent cooling load and sensible cooling load are handled separately by a desiccant-coated heat exchanger (DCHE)-based dehumidifier and a dew-point evaporative cooler (DEC). The DCHE first removes the undesired moisture of humid outdoor air by adsorption process. Subsequently, the DEC sensibly cools down the dehumidified air up to the desired temperature, maintaining the moisture level. Their performances are investigated numerically by analyzing the heat and mass transfer. Simulation has been carried out for DCHE and examined the output states of the process air, namely the dry-bulb temperature and humidity ratio. The following findings can be inferred from this study:
The equilibrium uptake reaches its saturated state at 0.33 during the dehumidification process and at 0.12 during the regeneration process at a given condition. The actual uptake follows the trajectory of the equilibrium uptake at a different rate.
MRC and Ql of the DCHE were markedly affected by varying the dry-bulb temperature and wet-bulb temperature, whereas COPth remained constant around 0.125. The MRC drop by 23.6% at higher temperature was observed due to the decreases in the humidity ratio.
As the wet-bulb temperature of the process air increases, the improved MRC and Ql of the DCHE were observed due to the increases in the humidity ratio.
For the DEC, dew-point effectiveness, wet-bulb effectiveness, and cooling capacity were linearly dependent on the dry-bulb temperature of the process air as well as the humidity ratio.
The higher dew-point effectiveness and the wet-bulb effectiveness can be expected at a higher humidity ratio, whereas higher cooling capacity can be obtained at a lower humidity ratio.
The proposed decoupling system has no moving parts and harmful refrigerant, rendering less maintenance compared with an existing cooling system. Furthermore, it is an energy-efficient means of latent and sensible cooling by adsorption process and water evaporation process with a waste heat source as compared with other conventional air-conditioning processes.
This work was supported by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIT) (No. CPS21171-110).
specific heat at constant pressure, J/(kg K) diffusion coefficient, m2/s heat transfer coefficient, W/(m2 K) latent heat evaporation, J/kg mass transfer coefficient, m/s half height of channel, m thermal conductivity, W/(m K) channel length, m mass flow rate, kg/s pressure, Pa heat transfer rate, W working air ratio temperature, °C velocity, m/s channel width, m x-coordinate, m thickness, mm density, kg/m3 humidity ratio, kg/kg dry air air dry air dew point water film inlet water vapor-air mixture plate primary air supply saturated water vapor wet air
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