Parameters for the two-domain nested ROMS model.
\r\n\tMass spectrometry has a significant potential to further advance the clinical analysis and be a diagnostic tool for pathology or being used in the operation room and enable fast detection of tumor's margin.
\r\n\tThis book aims to provide an insight into different application fields of mass spectrometry for clinical research and routine. Modern mass spectrometry enables analysis and quantitation of very complex samples such as serum or tissue proteome or lipidome and the mass spectrometry imaging complements tissue analysis in pathology departments.
\r\n\tFurthermore, mass spectrometry can be used in an operating room as a device for on-site detection of tumor margins and be a valuable help for the surgeon.
\r\n\t
\r\n\tMass spectrometry imaging of tissue is an advanced type of tissue analysis that complements and aids the pathology and the traditional tissue analysis by staining and optical microscopy.
The coastal seas are the most severely polluted waters in the world ocean. As shown in the preceding chapter, runoff from urban areas and agricultural fields, plus the deposition from the atmosphere, may lead to harmful algal bloom and the formation of dead zone due to hypoxia and eutrophication. In recent years, a new type of coastal pollution has been of great concern, that is, the nuclear pollution due to nuclear power plant failure.
\nHistorically, the most disastrous catastrophic nuclear disaster, in terms of cost and casualty, is the Chernobyl accident. The disaster began on April 26, 1986, with a late-night safety test at the fourth light water graphite moderated reactor at the Chernobyl Nuclear Power Plant, Ukrainian Soviet Socialist Republic of the former Soviet Union, which, however, ended with a destructive steam explosion that lofted plumes of fission products into the atmosphere (emission of radionuclides totals up to 13,000× \n
The impact of the Fukushima accident can never be overestimated; it has been ranked as the world’s worst nuclear accident in 25 years. The radionuclides have been widely spread with the winds and oceanic circulations; particularly, it is reported that they arrive above North America just 4 days after [4]. Although the emission is claimed to have been under control, the impact, particularly the impact on the oceans, remains [5, 6]. For example, the concentration of 137Cs off Japan, though has been on decline ever since the accident, remains as high as 100 times that before the accident by October 2014 [7]. By simulation, the radionuclides may reach the US coast in 4–5 years [8, 9] and then come back along the equator, impacting the coastal oceans in Southeast Asia. In this chapter, we focus on its impact on the East China coast, one of the most densely populated regions in the world. Since 137Cs has the longest life cycle (with a half-life period τ = 30 year), in the following, only 137Cs is considered.
\nBathymetry (in m) for the two-domain, one-way nesting model. Shown are the positions where the fluxes are calculated: a. Taiwan Strait, b. East of Taiwan, c. Ishigaki to Naha, d. Naha to Amami, e. Tokara Strait, f. Tsushima Strait. Also marked is a section of East China coast (green line).
Previously, the East China coast is believed to be not or less influenced by the accident [10]. Research during the past few years, however, shows that the Fukushima-originated 137Cs has already arrived in the China Seas. By Zhao et al. [11] and Rong et al. [12], it arrives in 2013 and will continue to accumulate in the following 5–6 years. Considering that the previous modeling studies do not take into account the background 137Cs distributions, and may generally have too coarse a resolution for the East China Sea, recently Rong and Liang [13] reexamine the problem with a highly resolved numerical model, plus a sequential updating strategy to assimilate the background 137Cs concentration, and reveal how the intruded radionuclide may move, evolve, reside, or disappear. This chapter is a summary of these results. The following is mainly based on Rong et al. [12] and Rong and Liang [13], where Sections 2 and 3 give a brief introduction of the model configuration and simulation strategy, Section 4 is a validation, Section 5 shows the results, and Section 6 concludes the study.
\nIn Rong and Liang [13], the Regional Ocean Modeling System (ROMS) is adopted for the simulation and prediction of the radionuclide transport. ROMS is a widely applied incompressible ocean model with free surface, hydrostatic, and Boussinesq approximations; it uses the Reynolds average Navier-Stokes equations as governing equations (e.g., [14]). In Cartesian coordinates (x,y,z), these equations are:
\nwhere \n
and parameterized turbulent fluxes; particularly, the vertical mixing is parameterized with a nonlocal, K-profile parameterization (KPP) scheme [15]. The closed set of equations are transformed into terrain-following coordinates (x, y, σ) and then solved using a split-explicit scheme.
\nA one-way nesting strategy is used, and hence two model domains are considered (Figure 1). The outer domain (L0) comprises the whole North Pacific Ocean, from the equator to Bering Strait. The inner domain (L1) covers the East China Sea (ECS) region. For both domains, there are 22 sigma levels in the vertical, while horizontally the resolutions for L0 and L1 are roughly 10.1 and 3.6 km, respectively. Other parameters are referred to Table 1. The bottom topography is extracted from the ETOPO1 data by National Oceanic and Atmosphere Administration (NOAA). A Hanning filter is applied to the topography to make sure that pressure gradient force is computed accurately [16].
\nDomain | \nMaximum depth (m) | \nMinimum depth (m) | \nLatitude (°N) | \nLongitude (°E) | \nResolution (km) | \nTime step (s) | \n
---|---|---|---|---|---|---|
L0 | \n5000 | \n15 | \n0–68 | \n96–288 | \n10.1 | \n900 | \n
L1 | \n5000 | \n10 | \n24–41 | \n117–135 | \n3.6 | \n90 | \n
Parameters for the two-domain nested ROMS model.
The horizontal boundaries for model L0 are all taken as closed. This makes sense because (1) Bering Strait is very narrow and shallow and (2) the equator is a dynamically closed boundary, though in reality there does exist cross-equator water exchange. This makes the long-time integration much reliable. For model L1, the boundary fluxes are supplied by the outputs from coarse model. The nesting is realized through the pointer-based ROMS2ROMS Matlab (Agrif) package [17]. To this model, tides are applied; specifically, the 10 tidal constituents, M2, S2, N2, K2, K1, O1, P1, Q1, Mf, and Mm, are considered here (data from Oregon State University; see [18, 19]). In the vertical direction, the no-flux condition is applied at the bottom. At the surface, wind stress, heat, and freshwater fluxes are prescribed. The stress and fluxes are from two datasets. Between January 2001 and August 2015, they are from the reanalysis data of National Centers for Environmental Prediction (NCEP, daily, 2.5° (lat) × 2.5° (lon)). After August 2015, the predicted data of Geophysical Fluid Dynamics Laboratory (GFDL, 3 hour, 2° (lat) × 2.5° (lon)) are used.
\nThe model is initialized with the fields of temperature, salinity, velocity, and sea surface height derived from the HYbrid Coordinate Ocean Model (HYCOM, 1/12°) reanalysis dataset. The 137Cs concentration is prescribed at two steps. (1) Initially, it is estimated using the data from International Atomic Energy Agency (IAEA,
During 1950–1990, plenty of radioactive substances had been poured into the oceans until the Chernobyl accident occurred and the Comprehensive Nuclear-Test-Ban Treaty was signed; it is believed that, by 1986, the 137Cs in the oceans has totaled 800 PBq [25]. Considering that the release in this accident is no more than 42 PBq [21], the major part of the 137Cs in the Pacific cannot be from Fukushima. This is particularly true for regions far away from the Plant. As an evidence, the IAEA data show that the average 137Cs concentration in the surface layer (0.5 m) of the North Pacific is 1.54 Bq/m3 during the decade before the accident, while previous studies neglecting the contribution from the background concentration (e.g., [9, 11, 23, 26]) reveal a maximum after-accident concentration less than 0.5 Bq/m3 in ECS, which is, obviously, far below the observation.
\nThe 137Cs distribution before the accident thence must be taken into account. In this study, two different simulations are performed. Run 1 as a control run does not have the background concentration; 5PBq of 137Cs is directly poured into the ocean at the accident time just as Hideyuki et al. [27] and Zhao et al. [11]. Run 1 runs from April 1, 2011 to March 31, 2021. Assume that the regions where the 137Cs concentration in Run 1 is less than 0.001 Bq/m3 are not affected by the pollutant directly poured into the Pacific. The observational data in these regions from January 2001 to February 2011 are then used as the observed 137Cs concentration. These data are assimilated into the model to form an optimal estimate of the field, which is taken as the background concentration for the next run, i.e., Run 2.
\nThe assimilation is through a scheme called sequential updating which, albeit simple, has been successfully utilized in the many operational ocean forecasts, such as in the forecast of the Iceland-Faeroe frontal variability [28, 31]. It is made up of two steps. First, use objective analysis (OA) to prepare the observational field for assimilation. The e-folding time and distance for OA are, respectively, 360 days and 40°. An error field is obtained accordingly. Second, an optimal interpolation (OI) is performed to combine the model output and the OAed field, with the inverse of the error field as the weight. The OI may be performed globally or pointwise. The two do not seem to make much difference; for ease to implement, the latter is hence adopted. In this way, the model output is sequentially updated with the observation.
\nTable 2 lists the observed surface (0.5 m) 137Cs concentrations in the China Seas before the accident [29] and our corresponding results. For all the six available observations, the mean relative error is 10.3%. Compared to the zero distribution in previous studies, our model works well to produce the 137Cs distribution before the accident.
\nLatitude | \nLongitude | \nObservations (Bq/m3) | \nSimulation results | \n|
---|---|---|---|---|
Concentration (Bq/m3) | \nRelative errors (%) | \n|||
32.01 | \n126.48 | \n1.01±0.06 | \n1.22 | \n20 | \n
36.05 | \n123.50 | \n1.10±0.07 | \n1.20 | \n10 | \n
20.50 | \n122.29 | \n1.14±0.07 | \n1.25 | \n9 | \n
29.64 | \n123.04 | \n1.32±0.13 | \n1.19 | \n−10 | \n
32.00 | \n124.00 | \n1.33±0.10 | \n1.19 | \n−11 | \n
18.00 | \n116.00 | \n1.42±0.09 | \n1.38 | \n−2 | \n
Comparison of surface layer (0.5 m) 137Cs radioactive concentration between the observations [29] and simulations in this study.
Since only the surface observation is available, the vertical 137Cs distribution has to be empirically set. We follow Tsumune et al. [30] to set:
\nwhere C (in Bq/m3) indicates the 137Cs concentration; particularly, C0 is the surface concentration. This, together with the measurements/estimates of the 137Cs concentration immediately after the accident, furnishes the initial condition for Run 2, which is used for the simulation and prediction.
\nOur simulated result has been compared with the data derived from the Simple Ocean Data Assimilation (SODA). Figure 2 shows the 2009 annual mean sea surface temperature (SST) and flow from our simulation (Figure 2a) and SODA (Figure 2b). It is easily seen that the major features of the SST have been well reproduced. For example, shown in the figure are the east-west asymmetry of the temperature in the tropic and the warm pool in the western equatorial Pacific. The large-scale circulations have also been well reproduced. The North Equatorial Current flows westward, encounters the west boundary, and forms the Kuroshio and the much more energetic current, the Ryukyu Current. Upon passing the Luzon, part of the Kuroshio may intrude into the northern South China Sea (SCS) in an anticyclonic form, but the mainstream keeps moving northward into the ECS. The Kuroshio in ECS branches to the northeast of Taiwan. One branch intrudes onto the shelf, forming the outer part of the Taiwan Warm Current and then merging back into the mainstream at a higher latitude. The Kuroshio flows out of the ECS through Tokara Strait, meeting the Oyashio Current off the Japan coast near Fukushima. It then flows eastward, in a meandering form, and makes the Kuroshio Extension System. These currents are evident in both Figure 2a and b, and they in these two subfigures are similar in magnitude and location. Our simulation of the large-scale system is therefore successful.
\nAnnual mean SST (shaded) and surface velocity (vector) of 2009 in North Pacific: (a) model output, (b) SODA data.
The comparison of the ECS circulation and SST is with the HYCOM product. Figure 3 shows the monthly mean (2006–2011) ECS SST and velocity. The left and right panels are, respectively, the simulated result and the HYCOM data. Note that East Asia has a monsoon climate; correspondingly, the ocean fields have strong seasonal variations. By comparing the SST and flow season by season, clearly the two panels agree well in both summer and winter, except in August when HYCOM displays a higher SST at the mouth of the Bohai Sea. The general features of the ECS circulation system have all been captured. For example, in winter (February), the coastal SST is lower than the open sea. West of Cheju Island, a warm tongue intrudes northwestward into the waters south of Shandong Peninsula. At this time, the Kuroshio, the Taiwan Warm Current, and the Tsushima Current are weak, and the Zhe-Min Coastal Current is southward. In summer (August, Figure 3c and d), the Kuroshio and its branch are strong, and the Zhe-Min Coastal Current flows northward. Scattered in the Yellow Sea are isolated cold patches; they are especially clear off the tips of Shandon Peninsula. These features are well known and have been successfully reproduced here. This completes the validation.
\nThe ECS SST (shaded) and velocity (vector) in winter and summer: (a) ROMS outputs, February; (b) HYCOM result, February; (c) ROMS outputs, August; (d) HYCOM result, August.
Table 3 shows the differences between simulations with (Run 2) and without (Run 1) assimilating the 137Cs background radioactive concentration in the North Pacific. Clearly, in Run 1, there are many regions where 137Cs has been observed, but the simulated concentration is zero. This simulation has been greatly improved in Run 2, where the concentrations at these locations are now close to the observations. As another issue, concentration may vary dramatically in 1 day (such as January 21, 2012, in Table 3). By comparing the observations from IAEA (124 different stations from June, 2011, to September, 2012, throughout the North Pacific) with the two Runs, it is found that the average relative deviation of the Run 1 simulations from the observations is 103.06%. In contrast, that of the Run 2 simulations is only 27.58%. If one recalls that the average relative interdiurnal variation of the observations is as high as 20.69%, the success of Run 2 is really remarkable. That is to say, the 137Cs simulation has been greatly improved with the background concentration assimilated.
\nDate | \nLatitude (°N) | \nLongitude (°E) | \nObservation (Bq/m3) | \nRun 1 (Bq/m3) | \nRun 2 (Bq/m3) | \n
---|---|---|---|---|---|
3/01/2012 | \n22.11 | \n191.46 | \n1.6 | \n0 | \n1.49 | \n
05/01/2012 | \n22.97 | \n179.98 | \n1.6 | \n0 | \n1.48 | \n
21/01/2012 | \n34.45 | \n130.08 | \n1.7 | \n0 | \n1.22 | \n
21/01/2012 | \n34.45 | \n130.08 | \n1.4 | \n0 | \n1.22 | \n
22/01/2012 | \n32.53 | \n132.98 | \n1.6 | \n0 | \n1.34 | \n
22/01/2012 | \n32.53 | \n132.98 | \n1.3 | \n0 | \n1.34 | \n
29/01/2012 | \n26.89 | \n182.06 | \n1.6 | \n0.01 | \n1.75 | \n
30/01/2012 | \n27.84 | \n189.1 | \n2.1 | \n0 | \n1.76 | \n
31/01/2012 | \n32.98 | \n197.06 | \n1.7 | \n4.08 | \n2.55 | \n
01/02/2012 | \n33.05 | \n204.72 | \n1.6 | \n0 | \n3.01 | \n
02/02/2012 | \n34.26 | \n213.1 | \n2 | \n0 | \n1.69 | \n
03/02/2012 | \n35.16 | \n220.91 | \n2.2 | \n0 | \n1.7 | \n
04/02/2012 | \n48.99 | \n219.18 | \n1.3 | \n0 | \n1.41 | \n
04/02/2012 | \n36.36 | \n228.83 | \n1.7 | \n0 | \n1.66 | \n
05/02/2012 | \n47.53 | \n228.13 | \n1.4 | \n0 | \n1.41 | \n
17/02/2012 | \n26.82 | \n173.34 | \n2.4 | \n0.12 | \n1.6 | \n
24/02/2012 | \n32.29 | \n206.67 | \n1.6 | \n0 | \n4.08 | \n
29/02/2012 | \n34.53 | \n175.9 | \n9.6 | \n1.81 | \n5.72 | \n
02/03/2012 | \n33.42 | \n196.11 | \n2.1 | \n1.61 | \n2.18 | \n
02/03/2012 | \n39.46 | \n177.47 | \n13.6 | \n4.53 | \n12.38 | \n
04/03/2012 | \n30.09 | \n211.27 | \n1.7 | \n0 | \n1.58 | \n
09/03/2012 | \n40.45 | \n133.84 | \n1.7 | \n0 | \n1.11 | \n
16/03/2012 | \n31.92 | \n223.18 | \n1.6 | \n0 | \n1.71 | \n
21/03/2012 | \n34.86 | \n177.27 | \n5.8 | \n3.77 | \n2.34 | \n
137Cs concentrations from the IAEA observations, Run 1 and Run 2 (January–March, 2012).
The ECS is a half-closed marginal sea in the Northwest Pacific, connected to the open ocean through several narrow waterways, which include Taiwan Strait, Tokara Strait, Tsushima Strait, and the channels between Taiwan and Yonaguni, Ishigaki and Nha, and Naha and Amami. To see how the Fukushima nuclear substances may intrude into ECS, the 137Cs fluxes across these six waterways are computed. From Figure 4, Taiwan Strait (Figure 4a) and the Taiwan-Yonaguni channel (Figure 4b) are the major straits that introduce the pollutants. The influx of 137Cs East of Taiwan is, on average, \n
Time series of the 137Cs fluxes across the six waterways indicated in Figure 1b (a–f; unit: \n\n\n\n\n10\n6\n\nBq\n\ns\n\n;\n\n positive values indicate fluxes into ECS; black lines are moving averages), and the total accumulation of nuclear pollutants in ECS (g; unit: PBq).
The other waterways near Taiwan include the section from Ishigaki to Naha (Figure 4c) and that from Naha to Amami Islands (Figure 4d). These sections are roughly parallel to the Kuroshio axis. With a water depth of 1500 m or so, they are also the main straits that connect ECS with Northwest Pacific. From the figure, it is seen that large amount of 137Cs is transported between Northwest Pacific and ECS. But because of the alignment, which is parallel to the Kuroshio path, the average fluxes in both waterways are orders smaller (respectively, \n
Tokara Strait (Figure 4e) and Tsushima Strait (Figure 4f) are the two waterways that outlet the ECS radionuclides. The flux through the latter is \n
To see the net influx of the pollutant, we take a cumulative sum of the fluxes through the six waterways from April 2011 to December 2025. Shown in Figure 4g is the cumulant. Note the negative value before 2012. That means there is a net outflux of nuclear substance during that period; in other words, the main part of 137Cs in the ocean has not arrived in ECS. After 2012, the nuclear substance begins to accumulate, though gradually, and reaches its peak in 2018 (0.13 PBq). In 2021, the sum is below zero again, implying that, in ECS, it takes about a decade for the radionuclide concentration to get back to its original level.
\nBecause of the dense population, we pay particular attention to the coastal regions. Figure 5a shows that the surface 137Cs concentration in ECS takes a maximum around 1.3–1.8 Bq/m3, depending on the location. Generally, it is high in the southeast of ECS and low in the northwest. The maximum is attained in 3 years after the accident along the Ryukyu Islands from Taiwan to Tokara Strait. On the whole, it peaks in 2014, except along the Zhejiang coast, where the peak appears 1 year later. In the Yellow Sea, the scenario looks much more complex. The maximal concentration is about 1.4–1.5 Bq/m3, but it varies with space and time. To the east of Harbor Lianyun, the maximum shows up in 2018; from Subei Shoal to Cheju Island, it appeared during 2014–2015; but from South Korea to Shandong Peninsula, it was attained in 2016. The concentrations in the Bohai Sea and Northern Yellow Sea and around the Shandong Peninsula remain at a level as that before the accident. That is to say, these regions are essentially not affected.
\n(a) Distribution of the simulated maximum surface 137Cs concentration (lines, in Bq/m3) and the year when it is attained (shaded). (b) Distribution of the maximal monthly mean 137Cs radioactive concentration (lines, in Bq/m3) and the months when the maximum is attained (shaded). The monthly mean is taken over the same months during 2014–2019. (c) Hovmöller diagram of the 137Cs concentration (shaded, in Bq/m3) between 25°N and 35°N (green line in Figure 1b) along the East China coast from 2011 to 2023.
Based on the above, the East China coast is most severely affected during the period 2014–2018. A particular observation is that there exists a local high 137Cs region (exceeding 1.45 Bq/m3) on the eastern side of the Subei Bank, a shallow water region off the middle Jiangsu coast. Figure 5c is a Hovmöller diagram between 25°N and 35°N along the East China coast. From it, the maxima occur in the winter of every year before 2019 in the northern part of Taiwan Strait, spreading northward all the way to Subei Bank till summer.
\nIn the Kuroshio region, the 137Cs concentration is high, and so is its horizontal gradient; that is to say, the strong current somehow functions to trap the radionuclide. Following the Kuroshio path to 35°N, one sees a concentration high in May-July. Along the Subei Coast, the maximum concentration is attained in August-November, while in the center of the Yellow Sea, the maximum takes place during September-November. For other regions such as the Bohai Sea and North Yellow Sea, the maximum appears in winter or early spring.
\nThe above suggests that the surface 137Cs concentration in ECS varies considerably from season to season. To see more about this, Figure 6 shows a sequence of the surface distribution in 2017. On the whole, the concentration displays a gradually decreasing trend northwestward, from roughly 1.7 Bq/m3 in the southeast to 1.4 Bq/m3 in the northwest. High-concentration water masses move mainly along the shelf break, following the Kuroshio path, from Taiwan toward Tokara Strait. That is to say, most of the nuclear substance influxing from east of Taiwan actually flows out of ECS; the major parts that affect the China coast are thence from within Taiwan Strait and through the Kuroshio Branch Current. In other words, they are with the Taiwan Warm Current (TWC), by which they are carried forth along the Zhe-Min Coast and Jiangsu Coast, and are finally transported out of ECS into the Sea of Japan through Tsushima Strait (Figure 6d). In the course, the remnants mostly stay along the Jiangsu Coast, leaving around the Subei Bank a high 137Cs concentration spot in summer (Figure 6a and d). We have also observed such a hotspot in other studies; see Chapter 2 for an example.
\nDistributions of the monthly mean surface 137Cs concentration in ECS for (a) January, (b) April, (c) July, and (d) October of 2017.
More than 6 years have passed since the Fukushima accident. The radionuclides from the disastrous nuclear leak have been identified in a lot of places in the world. Though there have been many studies, the impact of the accident on the local and global environment has not been well assessed. In this study, we find that the East China Sea (ECS), which was previously believed to be non- or less affected, actually has been full of the Fukushima pollutant, albeit the concentration is still far below a hazardous level.
\nUsing a two-domain, one-way nesting ROMS model, we have simulated and predicted the 137Cs distribution and evolution in the ECS. The outer domain encloses the whole North Pacific which largely avoids the open boundary problem and hence allows for a reliable longer integration. The external forcings (winds, heat and freshwater fluxes, etc.) are either real (from available NCEP reanalysis data) or derived from the GFDL predictions. Different from the previous studies, this model takes into account the background concentration of the radioactive 137Cs and has observations assimilated. The results have been carefully compared with the existing studies and observations and have been successfully validated.
\nBy the simulation and prediction, the accumulated 137Cs in the ECS reaches its peak in 2018; after 10 years, it falls back to the level before the accident. The straits on both of Taiwan form the main waterways that inlet the radionuclide into ECS, and Tokara Strait and Tsushima Strait are the two through which they leave the region. It is found that the 137Cs concentration, especially that along the coast, varies from season to season. Usually, the pollution is most severe in winter; the maximal concentration along the East China coast reaches 1.3–1.8 Bq/m3. A conspicuous feature is the existence of a hotspot of high 137Cs concentration in summer around the Subei Bank, a shallow water region off Jiangsu, the most populous province of China. The times that the maxima are attained vary from 2014 to 2018, depending on the latitude. Generally, the higher the latitude, the later the maximum is attained.
\nWe hope the above findings can help us to make policy for a rapid response to such kind of disasters. For example, should a more severe but similar leak happen again, we would first monitor the waterways on both sides of Taiwan, and the coastal regions such as the Subei Bank. Moreover, we can take actions in the waterways west and east of Taiwan in order to mitigate the situation and even get the pollution under control.
\nWe are grateful to IAEA for the 137Cs observations, to NGDC for the ETOPO1 data, to NOPP for the HYCOM data, and to NOAA for the SODA data, the reanalysis data, and the prediction data. This study was supported by the Jiangsu Provincial Government through 2015 Jiangsu Program for Innovation Research and Entrepreneurship Groups and through the Jiangsu Chair Professorship to XSL.
\nThe discovery of microwave cooking by Percy Spencer marked the dawn of a new era in microwave heating technology, which has gained huge attention in the scientific areas, especially in synthetic chemistry [1]. Numerous factors enabled the microwave technique to become a breakthrough technology in the complex synthesis process [2]. The significance of microwave heating for the synthesis of high-quality semiconducting nanomaterials, pristine or doped ones, is a subject that needs to be profoundly studied and explored due to its capability to revolutionize the semiconductor industry. The synthesis of high-quality nanocrystals primarily relies on controlled reactions of molecular precursors in a liquid medium at an adequate temperature in the presence of stabilizing agents [3, 4]. Most of the synthetic methods such as wet chemical process [5], emulsion methods, anti-solvent precipitation methods [6], have studied only the impact of the chemical process and parameters, on the properties of as-synthesized nanocrystals. Of late, the effect of additional external stimulations like microwave irradiation [7, 8], ultraviolet/visible light irradiation, ultrasound, etc. is also studied [5]. Microwave heating increases the rate of reaction, thereby considerably decreasing the reaction time without altering the kinetics and chemical reaction [9, 10, 11]. The rate accelerations caused by “specific microwave effect” as well as “non-thermal effects” have to be considered in the microwave heating mechanism. Baghbanzadeh et al. propose that microwave dielectric heating can be termed as “specific microwave effects” by which one can achieve rate accelerations that cannot be attained by the conventional methods [12]. In the case of “non-thermal microwave effects”, the heating mechanism arises as a result of the direct interaction of microwaves with specific molecules or materials in the reaction medium [2, 12]. Jacob et al. report that the enhancement rate of reaction with microwave heating compared to conventional heating is mainly due to the thermal effects which arise due to three significant factors. Firstly, the localized heating effect is a consequence of superheating phenomena due to the abundant ions present in the medium. Secondly, the molecular agitation due to lag of dipole, in following the fast-moving EM wave. Thirdly, increase of diffusion rate of reactants [13].
\nEarlier, the synthesis of high-quality semiconducting quantum dots was very tough and the process of doping at this length scale makes it even more challenging. Erwin et al. reported that the difficulty in doping at nanoscale regime is due to the difference in mechanisms involved in doping at bulk and at the nanoscale, while other reports in literature claim the process of ‘self-purification’ as the leading cause for de-doping during the growth process [14]. The major daunting challenges arise mostly due to the lack of a comprehensive understanding of all the fundamental mechanisms associated with dopants incorporation and the absence of reliable synthetic procedures where the temperature-dependent dopant impurity atoms diffusion will be minimal [15]. Another challenge involved in doping at the nanoscale is the inherent statistical inhomogeneity of dopants among the nanocrystals. The doped nanomaterials always tend to exhibit a broad range of dopant populations per nanocrystal, which results in effective inhomogeneity in concentration of dopants among nanocrystals. Providing a uniform and instantaneous heating during the reaction process can minimize this problem to a great extent [16]. In this context, microwave heating became the suitable thermal energy source for doping the semiconducting nanocrystals, as it provides rapid and instantaneous heating. Short reaction time, faster reaction rate, uniform volumetric heating, cost-effective and eco-friendly method are the other remarkable features which make microwave heating a prime superior choice over other conventional methods of heating like a hot plate, oil bath, etc. [8]. Moreover, heating by means of conventional methods always results in ‘a self-purification’ mechanism where the dopants are diffused towards the surface of nanocrystals at the time of growth [14]. By adopting microwave-assisted techniques the aforementioned problems encountered while doping at the nanoscale can be eliminated to a great extent and the synthesized products are found to excel both in quality as well as quantity [17].
\nMicrowave is an electromagnetic wave having low energy with a wavelength and a frequency in the range of 1 m to 1 mm and 300 MHz to 300 GHz, respectively as shown in Figure 1. Mainly, the laboratory and household microwave oven operate at a frequency of 2.45 GHz, which corresponds to a wavelength of 11.2 cm. It can travel at the speed of light (~30 cm/nanosecond) like any other electromagnetic wave and consists of electric and magnetic fields oscillating in a direction perpendicular to each other. One can also define it as a Multiphysics phenomenon in which the heating arises due to interaction between matter and electromagnetic radiation. In contrast to other conventional methods of heating, here the medium itself gets self-heated as a result of the alignment of molecular dipoles present in it with respect to the field associated. The electric and magnetic components in microwave interact with matter in different manners as discussed below [1].
\nSchematic representation of the electromagnetic spectrum in terms of wavelengths and frequencies.
The polar molecules are sensitive to an electric field, and thus as a result of force exerted by the field on the charged particles, they start to migrate or rotate in order to align along the field (Figure 2). Since the electric and magnetic components reverse direction rapidly with a frequency of 2.45 GHz, the electric dipoles have no time to orientate to the direction of electric filed. As a result, there occurs the angle between the orientation of the dipoles in space and the direction of the electric field and the energy loss by the dipoles occurs resulting in to rise of dielectric heating. Reflection, absorption, and transmission are the three modes by which the medium reacts to the electromagnetic waves, either in a single or combined fashion [18]. The effective dielectric loss factor for the dielectric heating can be expressed in terms of dipolar polarization, ionic conduction as follows
\nSchematic diagram of the interaction of an electric component of the microwave radiation with matter.
where\n
Like an electric field, a magnetic field interacts too with matter and induces heat through magnetic loss, joule heating, and so on. However, sufficient studies apart from dielectric heating are still very rare. Meanwhile, Cheng et al. reported that magnetic loss contributes significantly to microwave heating compared to dielectric heating [20]. The necessary physical processes generating heat energy as a result of interaction between material medium and the magnetic field component are the eddy current loss, hysteresis loss, and magnetic resonance loss [21, 22]. The overall losses that constitute the effective magnetic permeability (\n
where \n
Microwave heating has been the subject of interest for doping semiconductors at nanoscale owing to its ability to control the synthesis process explicitly. Apart from being cost-effective, the dielectric heating by microwave irradiation minimizes the dopant diffusion problem and provides quick reaction among precursors. The process of nucleation and growth of nanocrystals have been described in theories like LaMer burst nucleation [23], Watzky and Finke’s slow nucleation followed by autocatalytic growth [24], and LSW theory, etc. [25, 26]. Nucleation is the process where nuclei act as a template for nanocrystal growth. Uniform formation of nuclei throughout the growth medium defined as ‘homogeneous nucleation’ can be easily and efficiently achieved by microwave irradiation in contrast to conventional methods of heating. Volumetric heating provided by microwave irradiation raises the internal temperature of the whole medium simultaneously and homogeneously as illustrated in Figure 3. This favors a quick nucleation process which results in solution supersaturation leading to homogeneous nucleation. Microwave-assisted technique aids to measure, manipulate, and thereby optimize the nucleation process and parameters that in turn influences the stability of the synthesized particles along with an added advantage of automatic data recording [12]. Efficient doping is determined by the surface morphology and shape of nanocrystals and the presence of surfactants in the reaction medium. Temperature plays a significant role in molding the aforementioned factors [27]. This demands the necessity for a proper thermal energy source like microwave heating while synthesizing nanocrystals. High penetration depth (d) offered by microwave heating is yet another factor that distinguishes it from the conventional methods of heating. It is defined as the distance at which the microwave power reduces to 1/e of its incident power. It has inverse proportionality with oscillating frequency, dielectric, and magnetic loss factor. The formula for determinate a penetration depth (d) may be written as
\nSchematic illustration of main differences between the microwave heating (a) and traditional heating method (b).
where α is the absorption coefficient of microwaves,\n
The efficient absorption of the EM wave by the solvent is determined by its loss tangent factor. It is defined as the ability of a material to convert electromagnetic energy into heat energy at a given frequency and temperature [1]. A high value is desired for maximum absorption, however, heating aided by microwave radiation is achievable even in the presence of a low tan (δ) solvent provided there exists either a polar reactant or reagent such that the overall dielectric nature of the reaction medium favors the microwave heating. In the case of conventional heating methods, the transfer of heat is slow and inefficient, resulting in a huge temperature gradient owing to the different thermal conductivity of materials. However, in the case of microwave radiation, there is a direct coupling between the microwave energy and the molecules resulting in core volumetric heating. The most commonly used frequency of the microwave is 2.45 GHz, possessing an energy of 0.0016 eV, which is lower than that of Brownian motion and therefore insufficient to break the bonds. This property of microwaves makes them incapable of carrying out any unwanted reactions and thereby solely ensuring effective doping at nanoscale materials.
\nNanocrystals are broadly classified as nanoparticles and quantum dots. Generally, tiny particles of a dimension of 100 nm or below are termed as nanoparticles. However, quantum dots (QDs) are a class of nanomaterials with their charge carriers confined in all three dimensions of the length scale of exciton Bohr radius [28]. While doping the QDs, the dopants have a high tendency to come out of it due to the thermal diffusion because their size is in the nanometer range. This problem can be resolved greatly by having a comprehensive idea about the various mechanisms involved during doping and following a proper synthesis process [29]. However, various properties, including optical, magnetic, and electronic, of semiconducting quantum dots can be tailored in a desired fashion by the incorporation of impurity dopant atoms [30]. Moreover, this can also generate some new physical properties, including spin-polarizable excitonic photoluminescence, exciton storage, excitonic magnetic polaron formation, and magnetic circular dichroism so on. The proper incorporation of impurity atoms into the semiconducting QDs is a tough job but can be identified by observing the following features like red-shifted PL emission and large Zeeman splitting of excitonic excited states that are a result of strong exchange coupling between dopant and the carrier [31, 32]. In the year 2000, Mikulec et al. reported the most significant result on QDs doping; in which they reported manganese (Mn) doped CdSe nanocrystals with the evidential result obtained from electron paramagnetic resonance (EPR) [33]. Later, a variety of doped semiconducting material were reported by tailoring both the host atoms such as ZnS, PbS, MgO, Al2O3, α-Fe2O3, CdS, ZnSe, etc. and dopant atoms such as Mn, Cu, Ag, Fe, Zn, Cr, Er, etc. [34, 35]. However, there is a limitation to select the host system and the respective dopant atoms. Suppose, incorporation of Mn into nanocrystals of CdS and ZnSe easy but not into CdSe even though the bulk solubility almost equal to 50% for all three [27, 36].
\nDepending on dopants’ diffusivity, the dopant precursors are injected at different time intervals, suppose along with the host precursors or at the time of nucleation or growth as shown in Figure 4 [37]. The major problem involved in doping at the nanoscale is that many dopants fail to be incorporated within the host lattice and instead get adsorbed on the surface [38]. High formation energy for defects renders the impurity atoms to be thermodynamically unstable, resulting in the expulsion of dopants from the host lattice, in turn leading to self-purification [14, 27, 39]. Apart from thermodynamics, kinetics also play a significant role in determining the stability of added impurities in solution phase synthesis. Chen et al. have reported a detailed study regarding all the elemental processes involved with doping, such as surface adsorption, lattice incorporation, lattice diffusion, and lattice ejection as represented schematically in Figure 5 [40]. Maintenance of appropriate temperature is a crucial factor even in the phenomena mentioned above.
\nGeneral schematic model of the colloidal synthesis of doped quantum dots [37].
Schematic diagram showing temperature-dependent dopant lattice diffusion [37].
The high cost of commercially doped QDs is one reason that limits its wide range of applications. Therefore, cost-effective synthesis protocols need to be developed to produce high-quality doped QDs. This limitation and the ones mentioned above are lifted off using microwave heating for doping the QDs. It is also found to be an economical and eco-friendly method in line with green chemistry. Now let us discuss some semiconducting QDs systems where doping has been performed with microwave heating technique.
\nCdSe QDs is n-type intrinsically, and a flagship candidate in nanoscale research history shows several novel properties as a member of the II-VI binary semiconductor group. It was attractive to the researchers to demonstrate various optoelectronic applications as its energy band overlaps nicely with the solar energy spectrum [41]. The fundamental properties of CdSe are enhanced via doping, which further increases its demand in the semiconductor industry. However, doping of CdSe by Mn2+ ions is challenging due to the self-purification effect, as reported by Erwin et al. [27, 29, 42]. The doping process is mainly governed by the surface kinetic effect. Microwave heating helps one to have exquisite control over this surface kinetics that eases the doping process.
\nMeladom et al. developed a robust synthesis protocol for efficient doping of Mn2+ into CdSe QDs in an aqueous medium with mild microwave heating as a final step [17]. A household microwave oven was used to heat the CdSe QDs solution for 60 seconds duration with the set point of 450 W (operational frequency 2.45 GHz). This heating step was repeated three times by giving 5 minutes intervals. The motivation was to tune the electrical conductivity of CdSe QDs thin film by varying doping concentration only as the size of QDs kept similar for all the samples. Microwave heating improves the quality of QDs in terms of optical properties, which was confirmed by recording UV–vis absorbance and photoluminescence both excitation and emission spectra, as shown in Figure 6(a) and (b), respectively. In all the cases, peak intensities were enhanced and bandwidth reduced, which indicates the reduction of surface defects of QDs. The chemical composition of the doped CdSe QDs sample was confirmed with X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDS), and inductively coupled plasma - atomic emission spectroscopy (ICP-AES) measurements data. XPS result confirmed the efficient incorporation of Mn atoms as dopants inside the host CdSe QDs (Figure 7).
\nElectronic UV–vis (a) and photoluminescence (b) spectrum of Mn2+ doped CdSe QDs sample with and without microwave irradiation [17].
(a) Survey scan of the X-ray photoelectron spectrum of 2% Mn2+-doped CdSe QDs. (b, c) high-resolution spectrum of Cd 3d electrons depicting doublet splitting with binding energies separated by 6.9 eV and Se 3d, respectively. (d) the high-resolution spectrum of Mn 2p core electrons showing doublet splitting with binding energy separated by 10.9 eV [17].
Microwave-assisted synthesis has also been utilized by many research groups around the world to dope various other binary II-VI semiconductor-based nanocrystals. Molaei et al. reported the synthesis of copper (Cu) doped ZnSe nanocrystals in the aqueous medium to study the doping effect on the optical properties [43]. Synthesis of Mn2+ ion-doped ZnS quantum dots was reported by Joicy et al. using a rapid microwave irradiation step without any surfactants, which showed photocatalytic activity by observing photodegradation of methyl orange dye under UV light irradiation [44]. Here, the zinc blende crystal phase of ZnS was important for the efficient incorporation of Mn atoms. In the same year, Zhu et al reported the synthesis of of Mn-doped ZnS via green and rapid microwave-assisted approach and they also developed indapamide drug detector by recording room-temperature phosphorescence (RTP) with that doped material [45].
\nLater, Zhang et al. reported the aqueous synthesis of Mn and Cu doped ZnSe QDs by microwave radiation with higher quantum yields (QYs) and they have further extended this work to grow the white-light-emitting ZnSe/ZnS core/shell QDs via the co-doping of Mn and Cu [46]. Lead sulphide (PbS) QD are still emerging various applications in optoelectronics and its property was further tuned with a silver (Ag) atom doping. It is also reported by Shkir et al. that the bandgap of PbS QDs was increased with Ag atom incorporation, which was predicted without mentioning the influence of size variation between the samples used [47]. Recently, another work reported on facile microwave synthesis of CdS quantum dots doped with Cr atoms as impurity doping and they have studied various properties like structural, opto-dielectric, electrical, and so on [48].
\nVarious metal oxides nanomaterials play a major role in the development of different novel daily life applications in the fields of display, sensors, medicine, biomedical devices, agriculture, information technology, optical, energy, electronics, and so on. Efforts are ongoing to tune their properties and applications further with incorporating impurity as dopants. For that reason, microwave heating based synthesis protocols is developing as a potential alternative to the conventional heating based growth process. Kar et al. developed a microwave synthesis of rare-earth element Eu3+ doped tin oxide (SnO2) to tune the optical and electrical properties of the host [49]. Jamatia et al. reported the microwave-assisted synthesis of Fe doped ZnO nanoparticles to show their application in polymer light-emitting diodes [50]. The wurtzite hexagonal crystal phase of ZnO nanoparticles and incorporation of the Fe dopant into the host ZnO crystal lattice was confirmed via X-ray diffraction analysis. This report claimed that the bandgap modification of ZnO via Fe doping is estimated from the Tauc plot without considering the influence of size. Similarly, many spinel structured metal oxides were also doped with different transition metal ions via microwave heating based synthesis technique with tunable structural, morphological, optical, vibrational, and magnetic properties and different potential applications like phosphor-based forensic testing and many more [51, 52, 53, 54]. Interestingly, Er3+ doped α-Fe2O3 and Fe doped TiO2 nanoparticles were synthesized successfully with the help of microwave heating to study their crystal structure and optical properties [55, 56]. Recently, Yathisha et al. reported Zn2+ doped MgO nanoparticles utilizing microwave combustion route to study the influence on photovoltaic properties [57]. Therefore, microwave heating could explore further as a low-cost alternative synthesis protocol to design a new variant of nanomaterials.
\nLanthanum trifluoride (LaF3) is an ionic compound that is utilized as core-shell-up conversion nanoparticles (UCNPs) for different filed of applications like sensing, biomedical, and solar cells. Tek et al. reported Yb3+ ion-doped (active) and undoped (inert) LaF3 shell coatings on a 20% Yb, 2% Tm codoped hexagonal phase LaF3 core with the help of microwave -assisted synthesis route [58]. They observed higher optical enhancement of inert shell compared with the active shell at all prominent emission peaks, which is explained with the energy band diagram indicating the energy transfer pathways for the Yb3+ and Tm3+ − co-doping (Figure 8).
\n(a) UCPL data for core, active shell, and inert shell nanoparticles under 980 nm continuous-wave excitation. (b) Energy diagram showing the corresponding transition of UCPL of (a) where the energy transfer pathways for the Yb3+ and Tm3+-codoped up conversion nanoparticles are depicted [58].
Carbon-based materials like graphene can also be doped via microwave (MW) heating. Since the nanocarbon materials are found to be sensitive to microwave radiation [59, 60], the technique of MW heating was employed in the modification of graphene materials. It is also reported that by the use of microwave heating, hollow carbon nanospheres can be synthesized within a short time which can be effectively used as a host material for doping [61, 62]. Figure 9 shows the microwave-assisted approach to prepare metal/graphitic shell nanocrystals and CNT in a very short time using ordinary carbon precursor.
\nMicrowave-induced synthesis of Ni/graphitic-shell nanocrystals and graphitic hollow carbon Nano spheres [61].
The microwave-assisted technique facilitates the growth of heteroatom-doped graphene with better catalytic activity as well. Nitrogen doping up to 8.1% on graphene was achieved by Kwang et al within a minute with the aid of the microwave radiation. The binding configuration of nitrogen over graphitic basal planes can be varied with the irradiation power of microwave. The conductivity enhancement upto 300 Scm−1 was obtained in this case in comparison to nitrogen-doped via arc discharge method, nitrogen plasma process, etc. showing a lesser conductivity [63]. The dielectric heating of MW induces a high energy state that helps the graphitic basal plane to accommodate the dopants in order to convert graphite to N-doped graphene. The selective dielectric heating, which arises due to the difference in the dielectric constants of solvent and reactant can enhance the efficiency of doping without the rise of a thermal gradient [64]. The solid phase microwave-assisted synthetic method is adopted for the large-scale production of N-doped carbon nanodots (CNDs) using different citric acid/urea (C/U) weight ratios, which result in size variation of CNDs as shown in Figure 10 with the transmission electron microscope (TEM) images. The dopant ion concentration can be varied in a precise manner that results in N-doped graphene QDs and graphitic-carbon nitride quantum dots (g-CNQD). The doped material is found to exhibit a 38.7% quantum yield due to the presence of N and O rich edge groups resulting from the interaction of microwave on graphene [65].
\nHR-TEM micrographs of N-doped carbon nanodots (CNDs) samples prepared using the SPMA method for different citric acid /urea (C/U) weight ratios of 3/1 (a), 2/1 (b), 1/1 (c), 1/1.5 (d), 1/2 (e), and 1/3 (f). Inset shows the corresponding selected-area diffraction pattern [65].
Huge enhancement in the conductivity of microwave-assisted doped QDs has been reported in many pieces of literature. Microwave heating enables the tuning of electrical conductivity in a desired manner by proper incorporation of dopants into the desired locations of the host material. This is evidenced by the rise in the electrical conductivity to the order of 104 for 2% Mn2+ doped CdSe over undoped one as shown in Figure 11(a) [17]. Here, the conduction mechanism is controlled by the electric field-assisted thermal ionization of trapped charge carriers in CdSe QDs as described in Poole–Frenkel effect as shown in Figure 11(b) [66]. The bandgap has no role in the conductivity and the observed colossal conductivity enhancement is solely due to the concentration of Mn2+ dopant ions.
\n(a) Current–voltage characteristics of Mn-doped CdSe QDs for the samples with varying dopant concentrations. (b) Poole–Frenkel fitting for all samples with respective straight trend line [17].
The STM study performed on a monolayer device of Mn2+ doped CdSe QDs synthesized via microwave method founds to exhibit excellent memory characteristics as described in Figure 12 [17]. This memristor property is evident from Figure 12(b) where the doped CdSe QDs switched to a high conducting state at the bias of 2.5 V. It is also observed that the device switched back to its low conducting state when the tip swept towards 3 V and the ON/OFF ratio obtained was higher than 102. The reproducible nature of the resistive switching property over many cycles further confirms the reliability of the measurement. The threshold voltage at which the device switches to a high conducting state is found to be decreasing with an increase in the dopant concentration. Thus the notable electric bistability and the low threshold voltage of as synthesized doped CdSe QDs with the aid of simple and domestic microwave method promises its application in vivid area of future technologies which ensures minimum energy consumption per byte of the resistive data storage devices in future.
\n(a, b) The Tunneling current–voltage (I–V) characteristics of a monolayer of undoped and 0.2% Mn2+-doped CdSe QDs. Doped CdSe is showing low conducting state (OFF state, black line) and high conducting state (ON state, red line) for forward and backward voltage sweep direction respectively. (c, d) The differential conductance–voltage characteristics of a monolayer of undoped and 0.2% Mn2+-doped CdSe QDs respectively in their forward (black line) and backward (red line) sweep direction. The topographic images of bare Si(111).and monolayer of undoped CdSe QDs deposited on Si(111) are shown on the insets within (c) and (d) [17].
In this chapter, we mainly discussed the incorporation of impurity dopant atoms into a host semiconducting quantum dots system very efficiently using microwave heating strategy with the help of a large number of examples from the literature. It has also been observed that the zinc blend crystal phase is very efficient for the dopant incorporation than the hexagonal one. This also reflects that microwave heating can be utilized to synthesize various classes of doped zero-dimensional (0D) nanomaterials or quantum dots of many chalcogenides, oxides, carbon dots, and more with the large numbers of dopant atoms easily and more cheaply. Literature shows that the research related to two-dimensional (2D) transition metal dichalcogenides (TMDs) is booming up due to having tunable physical, electronic, and optoelectronic properties. Therefore, it would be intriguing to grow various 2D TMDs, both intrinsic and impurity-doped, via microwave heating, which will definitely reduce cost and different health and environmental hazards.
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