The total  efficiency limit of SSPVT collectors increases at higher w and at higher optical concentrations. The optimal spectral-splitting filter is defined by sharp lower- and upper-bound energies; the former always coincides with the bandgap of the cell, whereas the latter decreases at higher w. The total effective efficiency limit of SSPVT collectors is over 20% higher than those of either standalone PV modules or standalone ST collectors when w is in the range from 0.35 to 0.50 and up to 30% higher at w ≈ 0.4. This study provides a method for identifying the efficiency limits of ideal SSPVT collectors and reports these limits, along with guidance for selecting optimal PV materials and spectral-splitting filters under different conditions and in different applications.Spectral splitting is an approach to the design of hybrid photovoltaic-thermal (PVT) collectors that promises significant performance benefits. However, the ultimate efficiency limits, optimal PV cell materials and optical filters of spectral-splitting PVT (SSPVT) collectors remain unclear, with a lack of consensus in the literature. We develop an idealized model of SSPVT collectors and use this to determine their electrical and thermal efficiency limits, and to uncover how these limits can be approached through the selection of optimal PV cell materials and spectral-splitting filters. Assuming that thermal losses can be minimized, the efficiency limit, optimal PV material and optimal filter all depend strongly on a coefficient  Scientists have developed a method for predicting and optimizing the performance of hybrid photovoltaic-thermal (PVT) collectors. Photovoltaic (PV) cells use only a portion of the solar spectrum to generate electricity, limiting the efficiency of single-junction cells to 20–30%. PVT collectors improve on this overall solar utilization by recovering unused thermal energy and delivering a useful thermal output. Spectral splitting is a promising approach for improving the performance of PVT collectors by exploiting the spectral sensitivity of PV cells. Christos Markides and Gan Huang from Imperial College London, in collaboration with Kai Wang from Zhejiang University, have developed a comprehensive framework for modelling spectral-splitting PVT collectors, which they have used to determine the efficiency limits of such collectors and how these limits can be approached through the selection of optimal PV cell materials and spectral-splitting optical filters. Global PV capacity crossed the milestone of 500 GW in 2018, overtaking solar water-heating collector capacity for the first time and continuing to grow, reaching 630 GW in 20194. Common solar cell materials include semiconductors, such as Si5, CdTe6, GaAs7, CIGS8 and perovskites9. Single-junction Si cells remain dominant within the global PV market owing to their low costs and mature manufacturing processes10.Solar energy is a clean and abundant energy source. Current methods of harvesting solar energy include solar thermal (ST) and photovoltaic (PV) technologies. The latter have attracted considerable interest in recent decades, as PV cells are able to convert solar energy directly into valuable electricity without noise and moving parts in simple systems that are easy to install11. Other solar cells also experience this partial spectral sensitivity12. The unused portion of the solar spectrum dissipates as waste heat in solar cells, increasing their operating temperature and leading to a monotonic deterioration in their electrical efficiency. The electrical efficiency of mono- and polycrystalline Si cells typically decreases by 4.0–6.5% for every 10 °C increase in operating temperature13. Radiative cooling approaches can be used to further improve these efficiencies14.Only an incident photon with a higher energy level than the bandgap of solar cells can activate an electron-hole pair and generate electricity. Thus, solar cells are sensitive to only a part of the solar spectrum that can be converted to electricity. The theoretical efficiency limit of Si solar cells is ~30% under one sun according to the seminal research of Shockley and Quisser16. This makes PVT collectors significantly more efficient overall than standalone PV modules18. However, in these arrangements, the thermal absorber is designed to be in good thermal contact with the PV cells, leading to similar PV cell and absorber operating temperatures and compromising the PV efficiency if a higher-temperature thermal output is pursued. This ‘thermal coupling’ feature of conventional PVT collectors unavoidably limits their performance and applications.PVT technology has been attracting increased interest as a solution that promotes broader solar utilization. Flat-plate PVT collectors are the most common design, most of which are based on attaching a thermal absorber to the back of PV modules. The thermal absorber cools the PV cells but also harvests waste heat from the cells as useful thermal energy, which can be utilized downstream for domestic hot water or space heatingSpectral splitting, as proposed in various forms as early as the 1970s and 1980s, is a promising approach for improving the performance of PVT collectors by exploiting the partial spectral sensitivity of PV cells while minimizing, through alternative designs, the thermal coupling in these collectors. The conceptual principle of a spectral-splitting PVT (SSPVT) collector is illustrated in Fig. λ1 and λ2, of which only the visible part of the spectrum (λ1 < λ < λ2) is sent to the PV cells for electricity generation, while the rest of the spectrum in the ultraviolet (UV) and IR regions is directed to a thermal absorber. In this way, both the PV cells and thermal absorber can operate at appropriate temperatures and the SSPVT collector is able to produce very high-temperature heat without compromising the PV efficiency. High-temperature heat can be utilized in various industrial applications or, if high enough, for thermal power generation.As shown in this figure, in SSPVT collector concepts, the solar spectrum is separated into three different parts at wavelengths 23. Selectively absorptive filters, on the other hand, allow only part of the solar spectrum to pass through to the PV cells and directly absorb the rest of the spectrum. Liquid flows, which can act as both optical filters and heat transfer fluids, have been proposed as promising selectively absorptive filters. Within this category, nanofluids (nanoparticle suspensions) are promising and emergent selectively absorptive filter types for SSPVT collectors, which have attracted significant interest recently27. The state-of-the-art SSPVT technology based on emerging nanomaterials has been summarized in a recent review article28.Current SSPVT collector concepts typically employ one of two spectral-splitting approaches based on either selectively reflective or absorptive optical filters. Selectively reflective filters, which are used widely in concentrating PVT collector designs, allow part of the solar spectrum to pass through to the PV cells and reflect the rest to a thermal absorberλ1 and λ2 in Fig. 25, 732–1067 nm in Crisostomo et al.29, 640–1127 nm in Bierman et al.30, 600–1150 nm in Otanicar et al.31, 400–1100 nm in Shou et al.32, and 300–1100 nm in Soule et al.19. This widespread reveals a lack of consensus in the literature concerning the definition of the optimal filter, even for the most common solar cell. Furthermore, although many PV materials  have been applied to SSPVT collector applications, the optimal PV material for a SSPVT collector also remains unclear. Therefore, the efficiency limits of SSPVT collectors, which depend strongly on the PV material and spectral-splitting filter, remain unclear.The optical characteristics of the spectrum-splitting filter are crucial to the performance of SSPVT collectors, as this strongly determines the electrical, thermal and total  efficiencies of such collectors. A number of real filters have been developed to date, including thin-film reflective and nanofluid absorptive filters. However, identifying the optimum filter, i.e., optimal values of In this study, we develop a methodology and a model capable of rationally identifying the optimal PV cell material and spectral-splitting optical filter in different applications and of predicting the ideal efficiency limits of concentrating SSPVT collectors employing these cells and filters. The performance of SSPVT collectors in a typical application of thermal power generation is analysed. We also consider the performance of SSPVT collectors with common PV materials  and seek to identify corresponding optimal spectral-splitting filters.EL < E < EH are directed to an ideal PV cell with a bandgap energy Eg and the rest  are absorbed by an ideal thermal absorber to generate high-temperature thermal energy. Here, EL and EH are defined as the lower and upper bounds of the optical filter (in eV), and the range between EL and EH is referred to as the ‘filter window’. Of note is that an ideal PV cell will inevitably generate waste heat even if only the desired photons are directed to the cell. The waste heat in the PV cell is challenging to recover in the form of high-temperature thermal energy both due to the low temperature limits of PV cells, which assign a low value (heat grade) to this energy, and due to the associated complexity and cost of related solutions. The SSPVT collector design employed in this study, therefore, does not attempt to recover the low-temperature waste heat in the PV cell, such that this heat is ultimately rejected to the environment.A physical model of an ideal concentrating SSPVT collector is shown in Fig. The mathematical model in this paper is based on the following assumptions: (1) the PV cell is ideal, has a quantum efficiency of 1 and is maintained at 300 K via efficient heat rejection; 2) the optical concentrator is ideal and concentrates the incident sunlight (AM1.5) with negligible optical losses; and (3) the spectral-splitting filter ideally separates the incident photons into two parts, as shown in Fig.  the optiC is the concentration ratio, Gs(λ) is the incident solar spectral irradiance based on AM1.5, VPV is the applied cell voltage and IPV is the current in a single-junction solar cell34:TPV is the temperature of the PV cell and Eg is the bandgap energy of the cell. Detailed equations are introduced in the final section of this paper .The electrical efficiency of the  PV cell can be determined from:ηth,loss is the fraction of the total incident solar energy that is lost from the collector through heat transfer and Eth is the useful thermal energy gained by the thermal absorber.The thermal efficiency of the thermal absorber can be determined from:C = 100, 210, 1000 and 45,000) are investigated in this study, where C = 210 and 45,000 are taken as limits for linear concentrators  and circular concentrators , respectively35, and C = 100 is taken as a typical concentration ratio that can be achieved in practice by current parabolic trough concentrators or parabolic dish concentrators36. Finally, C = 1000 is taken as a typical concentration ratio of current solar towers36. The heat loss ratio ηth,loss decreases as the concentration ratio increases. The heat loss can also be significantly suppressed via emissivity control and evacuation37. Thus, the term ηth,loss for a concentrating collector can be made small at high concentration ratios, e.g., ηth,loss of a typical evacuated tube ST collector is <1% at a concentration ratio of 100 when the output temperature is ~400 °C above the ambient temperature38. Therefore, this term is assumed to be small in this work. It is noted, however, that in cases where this assumption is not valid, this framework can be easily extended to account for realistic losses if desired, e.g., by expressing ηth,loss in terms of suitable radiative and convective loss terms.Four concentration ratios , a cost value  or a ratio of environmental benefits . The optimum filter in a SSPVT collector depends on the definition of the total effective efficiency, i.e., the value of w. In this study, the total effective efficiency is also treated as the merit function in the optimization of the filter and the bandgap energy of the PV cell.As the values of heat and electricity are different and depend strongly on the application and the demands for these two energy vectors, a total effective efficiency can be defined as:ηtot of SSPVT collectors reaches a maximum value for optimal values of EL and EH. Figure w at a concentration ratio of C = 100. The curve generated for w = 0 corresponds to the electrical efficiency limit of standalone concentrating PV cells with different bandgap energies. The thermal energy becomes more valuable (relative to electricity) as the weight coefficient w increases, leading to a significant increase in the limit of the total effective efficiency, as similar to pure ST collectors vis-a-vis PV modules, the conversion of sunlight to heat is more efficient than its conversion to electricity. At an extreme scenario of w = 1, the thermal energy has an equal value to that of the generated electricity. In this case, a ST collector delivers the highest efficiency of 100% (as we assume no heat losses at high concentrations). The total effective efficiency limit is sensitive to the solar cell bandgap energy when w is in the range from 0 to 0.5, corresponding to scenarios in which electricity is at least twice as valuable as thermal energy. The bandgap energy has a slight influence on the total effective efficiency limit when w is larger than 0.8, indicating that PV cells are less necessary when the thermal energy delivered is of comparable value to electricity. In summary, the total effective efficiency limit of SSPVT collectors depends strongly on not only the solar cell bandgap energy but also the relative value of thermal energy relative to that of electricity .The total effective efficiency C = 210, 1000 and 45,000 are shown in Fig. w is smaller than 0.5, but when w is above 0.5, this effect is minor. The standalone PV cell efficiency limit increases from 38% to 45% as C increases from 100 to 45,000. The limit of the total effective efficiency of SSPVT collectors thus increases as the concentration increases owing to the increased PV efficiency when w is below 0.5. In either case, the total effective efficiency limit is more sensitive than that of the concentration ratio, which suggests that the application is a greater determinant of performance than the solar conditions or the concentrating optical design of the system.The total effective efficiency limits of SSPVT collectors with higher concentration ratios of w, to optimize the collector and maximize the total effective efficiency. Thus, a SSPVT collector always has a higher efficiency than either standalone PV modules or standalone ST collectors when 0 < w < 1. The total effective efficiency limit of SSPVT collectors equals that of standalone PV modules at w = 0, and that of standalone ST collectors at w = 1. The total effective efficiency limit of SSPVT collectors also increases as the concentration ratio C increases.Also of interest are comparisons of the efficiency limits of SSPVT collectors to those of standalone single-junction PV modules and ST collectors in Fig. w increases, whereas the relative improvement of SSPVT collectors over standalone PV modules increases as w increases, as shown in Fig. w decrease the advantage offered by SSPVT collectors over either standalone ST collectors or standalone PV modules. In particular, SSPVT collectors have a considerable advantage over standalone PV modules and standalone ST collectors when w is within a given range that depends on the concentration. For example, the total effective efficiency limit of SSPVT collectors at the maximum concentration ratio  is over 20% higher (in relative terms) than those of both standalone PV modules and standalone ST collectors when w is between 0.35 and 0.50.The relative improvement offered by SSPVT collectors over ST collectors, in terms of total efficiency, decreases as C = 100, 210, 1000 and 45,000 are 33%, 32%, 31% and 29% higher than those of standalone PV modules or ST collectors when w = 0.38, 0.39, 0.40 and 0.45, respectively, as shown in Fig. Of particular interest are the crossing points in Fig. Eg and the corresponding optimal lower-bound EL and upper-bound EH for different w and different concentration ratios C are shown in Fig. EL always coincides with the bandgap energy Eg. An incident photon can convert only a part of its energy equalling the PV bandgap energy to electricity, while the rest of the photon energy is dissipated as waste heat. Low-energy photons with an energy close to the PV bandgap can be fully utilized by PV cells at high electrical conversion efficiency (relative to high-energy photons). Thus, the optimal lower-bound EL always equals the bandgap energy Eg to maximize the total effective efficiency, considering that thermal energy is normally less valuable than electricity.The optimal solar cell bandgap energy EH decreases at higher values of w. High-energy photons (in the UV spectrum) have a lower electrical conversion efficiency than low-energy photons but also have a high thermal conversion efficiency. Thus, the optimal EH decreases as w increases, such that more high-energy photons are directed to the thermal absorber as the value of thermal energy increases. The upper limit of Eh is ~4.4 eV, which is the maximum energy of the incident photons. The optimal bandgap energy for SSPVT collectors increases from 1.05 to 2.05 eV as w increases from 0 to 0.8 when C = 45,000. Once the values of w and C of an application are specified, both the optimal PV material  and the optimal filter  can be selected by referring to Fig. λ(nm) = 1240/E(eV).The optimal upper-bound w depend strongly on the specific application. In this section, the performance of SSPVT collectors in power-generation applications is considered, in which the thermal energy produced by the SSPVT collectors is used downstream to generate secondary electricity via a heat engine. In this case, the total electricity generated by this combined system is equal to that generated by the PV cells in the SSPVT collectors plus that generated by the secondary heat-to-work conversion process.The definition and value of w in Eq. (Th is the output (heat delivery) temperature of the SSPVT collector, which is considered to be approximately equal to the temperature of the thermal absorber of the collector, and Tc is the cold sink temperature of the heat engine (Tc = 300 K in this study). The term w in Eq.  are shown in Fig. 39. At temperatures of 400, 500, 600 and 673 K, we obtain corresponding w-values of 0.25, 0.40, 0.50 and 0.55, respectively. From this figure, we observe that integrating SSPVT collectors with ST power generation leads in all cases to significantly higher total electrical efficiencies than standalone PV systems. The total effective efficiency limit of SSPVT collectors increases as the output temperature Th increases, driven by the increase in the value of higher-temperature heat. The total effective efficiency limit reaches a peak at 63% at the highest temperature of Th = 673 K (w = 0.55) but is also sensitive to the bandgap energy of the PV cell material. Seven PV materials with different bandgap energies are marked on the abscissa of Fig. 40. The optimal PV materials for the SSPVT collector with Th = 400 K (w = 0.25) are GaAs and CdTe, which have an efficiency limit of 51%. The selection of the PV material is crucial to SSPVT collectors when Th = 400 K (w = 0.25), as the efficiency limit is sensitive to the bandgap energy of the cell. The optimal bandgap energy moves to higher values as the output temperature increases. The optimal PV material for SSPVT collectors with Th = 673 K (w = 0.55) is GaInP. The total effective efficiency limit of SSPVT collectors with Th = 673 K varies in the range 55–63%, as the bandgap is varied . The selection of the PV material has a slight influence (only 7%) on the total effective efficiency limit of SSPVT collectors when Th = 673 K, as indicated in Fig. The total SSPVT effective efficiencies at the maximum concentration ratio for different ST output temperatures  are shown in Fig. EL always equals the bandgap energy of the PV material, whereas the optimal upper-bound EH depends not only on the PV material but also on the temperature of the thermal output. Higher-temperature heat is more valuable, as it can be converted to electricity with a higher efficiency. Therefore, the optical filter window narrows as Th increases from 400 K to 673 K (as w increases from 0.25 to 0.55), indicating that more solar energy is directed to the thermal absorber as less solar energy is directed to the PV cells. For example, the optimal filter for the Si-based SSPVT collector directs only 19% of the incident solar energy to the thermal absorber when Th = 400 K, but it directs 62% of the solar energy to the thermal absorber when Th = 673 K.The optimal spectral-splitting filters at four different output temperatures , only 17% of the solar energy is directed to the PV cells and the effect of concentration becomes negligible, as shown in the figure.The total effective efficiency limits of SSPVT collectors in power-generation applications for different concentration ratios are shown in Fig. Th. The total efficiency limit of SSPVT collectors at lower temperatures  is close to double that of ideal ST collectors but is only 3–4% higher, in relative terms, when the output temperature is as high as 1000 K.Furthermore, from Fig. w = 0.2, 0.4, 0.6 and 0.8 are shown in Fig. EL, EH, ηtot), which indicate the optimal lower bounds, upper bounds and maximum total effective efficiencies for different w. We note that the value of w has a significant influence on the total effective efficiency limit and on the optimal filter. The optimal lower bound and upper bound are 1.12 eV and 4.34 eV, respectively, for w = 0.2, as shown in Fig. w = 0.2, corresponding to a scenario where electricity is much more valuable than heat. The optimal upper-bound EH decreases from 4.34 to 2.64 eV as w increases from 0.2 to 0.4. In this case, more photons with higher energy (in the UV region) are sent to the thermal absorber as the value of thermal energy increases. Interestingly, the optimal lower-bound EL is always 1.12 eV for all scenarios, which coincides with the bandgap energy of Si. The optimal upper-bound EH approaches the lower-bound EL for the case of w = 0.8, as shown in Fig. Si is the most common PV material and accounts for over 90% of the current global PV market. The bandgap energy of Si is 1.12 eV (1110 nm). Distributions of the total effective efficiency limits of SSPTV collectors with different spectral-splitting filters for w and concentrations C. The total effective efficiency of Si-based SSPVT collectors increases significantly at higher w or C. The electrical efficiency of the standalone Si cell increases from 39% to 45% as C increases from 100 to 45,000. The advantage of Si-based SSPVT collectors over standalone ideal Si cell modules becomes larger as w increases and the performance of these SSPVT collectors approaches that of the ideal ST collector.Figure w is <0.2, the optical filter window spans the maximum extent from 1.12 to 4.34 eV (290 to 1110 nm), which coincides with the entire spectral sensitivity  range of Si solar cells. In this case, the maximum extent of the spectrum is directed to the cells, because electricity is more valuable than thermal energy. The filter window for the Si-based collector ‘closes’ at w = 0.72 and 0.85 when C = 100 and 45,000, respectively, which means that all the solar radiation is directed to the thermal absorber and the PV cell is no longer necessary, corresponding to the scenario in which thermal energy has an equivalent value to electricity. The optimal lower-bound EL always equals the bandgap energy of Si 1.12 eV (1110 nm) for all scenarios. The optimal upper-bound EH increases as the concentration ratio increases. Figure w and C in specific applications.The optimal filters of Si-based SSPVT collectors are shown in Fig. 2 (CIGS) and GaInP40. As mentioned earlier, Si cells dominate the current global PV market, followed by CdTe and CIGS41. The bandgap energies of the above materials are 1.12 eV (Si), 1.42 eV (GaAs), 1.43 eV (CdTe) and 1.81 eV (GaInP)40. The bandgap energy of CIGS can be continuously tuned from ≈1.0 to 2.4 eV by varying the ratios of In/Ga and Se/S40. The bandgap of the current record-efficiency CIGS solar cells is ~1.10–1.13 eV43, which is close to the bandgap energy of Si. Thus, the bandgap energy of CIGS is taken as 1.12 eV in this section. Ge (0.66 eV) and GaP (2.25 eV) are also included in the analysis considering their utilization of a very different region of the solar spectrum. In addition, in the context of PVT applications, GaAs has good performance at high temperatures44.Other common PV materials include Si, GaAs, CdTe, Cuw or C increases for all PV materials. The performance of SSPVT collectors with different materials becomes increasingly similar as w increases, with the material having a very slight influence on performance for w > 0.7. Below this value of w, the optimal PV cell material depends strongly on the values of w and C, so the material needs to be selected carefully, because it determines the ultimate efficiency limit of SSPVT collectors. In general, Si, CIGS, CdTe and GaAs are more suitable for use in SSPVT collectors than in other solar cells, delivering a higher total effective efficiency limit. GaInP gradually catches up as w increases. Ge and GaP are largely unsuitable PV cell materials for SSPVT collectors due to their bandgap energies, which are either too low or too high.The total effective efficiency limits of SSPVT collectors employing the aforementioned PV materials are shown in Fig. EL always equals the bandgap energy of the PV material, which is consistent with earlier observations in this study. Similarly, the upper-bound EH decreases as w increases, as observed above. The optical filter windows for all PV materials finally cross at certain ‘closing’ points  as w increases. The filter windows for Si/CIGS, CdTe/GaAs and GaInP are close at w = 0.85, 0.87 and 0.89, respectively, when C = 45,000. The SSPVT collectors operate effectively as ST collectors when w exceeds the value at the closing point. The closing point moves rightward as the bandgap energy of the PV cell increases, as shown in Fig. w and C).The optimal spectral-splitting filters for different PV materials are shown in Fig. ηPV_idl is the efficiency of ideal PV cells with Shockley–Queisser (S-Q) limit efficiencies, ηth_idl is the efficiency of an ideal thermal absorber with 100% efficiency  and those of ideal materials, which will lead to a performance loss. In this section, we consider the impact of non-ideal PV cells, optical filters and thermal absorbers on real SSPVT collector performance. For this purpose, we define two coefficients that describe the non-ideality of real components, one in relation to the PV cells and one to the thermal absorber:elow Eq. ), and ηPICPV and ICth, on the total effective efficiency limit of SSPVT collectors is shown in Fig. C = 100, which is readily realized in practice.The influence of the two coefficients above, ICPV and ICth on the SSPVT total effective efficiency limit depends strongly on the value of w. Compared to ICth, ICPV has a more significant influence on this efficiency limit for w = 0.2, corresponding to scenarios in which electricity is considerably more valuable than heat. Both ICPV and ICth affect the total effective efficiency limit when w = 0.4 and ICth becomes the main influencing factor when w = 0.6. Therefore, to improve the total effective efficiency limit of real SSPVT systems with non-ideal PV cells and thermal absorbers, the designer should focus on improving ICPV when w is small  and on improving ICth when w is large .We note that the influence of EL and ΔEH) are:EL_opt and EH_opt are the lower and upper bounds of the ideal optimal filter, respectively, and EL_act and EH_act are the real bounds. The influence of ΔEL and ΔEH (units: eV) on the total effective efficiency limit of SSPVT collectors is shown in Fig. Beyond the PV cells and thermal absorber, the optical filter is another component that can affect the performance of real SSPVT collectors, because, as above, the lower and upper bounds of real filters may deviate from their optimal ideal values. The deviations of these two bounds  for Si cells. Furthermore, a positive deviation of EL from its ideal value  leads to a more significant reduction in the total efficiency limit than a negative deviation for w = 0.2, whereas a negative deviation  leads to a more significant reduction in the efficiency limit for w = 0.6.The deviation Δw.Other non-ideal factors that can influence the present results include the PV material bandgap energy, which can shift with temperature if the cells are not sufficiently cooled, and the concentrator optical efficiency. In practice, it may be difficult to cool the PV cells to 300 K at high solar concentrations, with increased PV temperatures usually decreasing the bandgap energy. For example, the bandgap of Si decreases by ≈3% from 1.12 to 1.09 eV as the temperature increases from 300 to 400 K. The effect of this PV bandgap energy shift on the total effective efficiency limits of SSPVT collectors can be seen in Fig. We have proposed a framework for predicting the performance of SSPVT collectors, with the aims of identifying the total effective efficiency limits of SSPVT collectors and of providing detailed guidance for selecting optimal PV materials and optimal spectral-splitting filters capable of delivering a combined thermal and electrical performance that reaches the efficiency limits of this technology.w, which considers the relative value of thermal energy to that of electricity, has a significant influence on the total effective efficiency limits, the optimal PV cell material and the optimal spectral-splitting filter of ideal SSPVT collectors. The limit of the total effective efficiency increases as either w or the concentration ratio, C, increase, but is less sensitive to the latter, which suggests that the application is a greater determinant of the ultimate performance of such systems. The total effective efficiency of SSPVT collectors is also particularly sensitive to the bandgap energy of the PV material when w is <0.5, corresponding to scenarios in which the electricity is at least twice as valuable as the generated thermal energy. The optimal lower-bound absorption energy of the spectral-splitting filter always equals the bandgap energy of the employed PV material, whereas the upper-bound filter absorption energy decreases as w increases. The optical filter window between the two bounds becomes narrower at higher w, indicating that more solar energy is directed to the thermal absorber as the thermal output attains a higher value.The value of a weighing coefficient w increases from zero . Specifically, when w = 1, SSPVT collectors have ~2.5 times the total efficiency of PV modules. On the other hand, the advantage of ideal SSPVT collectors over ST collectors improves as w decreases from unity (which is associated with lower Th). The total effective efficiency limit of SSPVT collectors in cogeneration applications requiring lower-temperature heat  is approximately double that of ideal ST collectors when considering the relative thermodynamic values of electricity and heat. At intermediate temperatures, SSPVT collectors have the greatest advantage over either of these standalone conventional technologies and systems when both are available for selection and installation. The total effective efficiency limit of SSPVT collectors is over 20% higher than those of both standalone PV modules and ST collectors when w is in the range from 0.35 to 0.50 and is up to 30% higher at w ≈ 0.4.SSPVT collectors have an advantage over PV modules that grows monotonically as w and C, which in turn are set by the specifics of the application. Suitable PV materials for high-concentration SSPVT collectors are Si and CIGS when w < 0.22 or CdTe and GaAs when w > 0.22; these same solar cell materials also appear to be the best choice for lower-concentration SSPVT collectors, with GaInP appearing as an additional promising material at higher w (above ≈0.4). On the other hand, Ge and GaP emerge from this analysis as less suitable for SSPVT collectors due to their extreme (low/high) bandgap energies. Finally, CIGS cells are considered particularly promising for SSPVT collector applications, owing to their adjustable bandgap energy within the range of ≈1.0–2.4 eV.The optimal PV cell material for SSPVT collectors depends strongly on the values of C and weighting coefficient w.The optimal lower and upper bounds of the spectral-splitting filter depend on the PV material, concentration ratio In summary, detailed maps and other results in this study can assist designers in selecting appropriate PV cell materials and spectral-splitting optical filters, depending on the conditions and application, to achieve optimal overall performance accounting for both energy vectors  being provided by these collectors and wider systems.λ, can be calculated from:h is Planck’s constant, c is the speed of light, q is the elementary charge and λ is the photon wavelength.In the model of SSPVT collectors in the main text, the PV electrical efficiency is obtained by a classical PV model. The energy of a photon, corresponding to its wavelength 11:Gs(λ) is the incident solar spectral irradiance based on AM1.5, and λ1 and λ2 are the lower- and upper-bound wavelengths of the optical filter, corresponding to EH and EL, respectively. The radiative recombination loss of electron-hole pairs can be ignored in an ideal PV cell. Furthermore, the solar concentration ratio C is defined as C = Aa/Ar, where Aa is the aperture area of the optical concentrator and Ar is the receiver area.The number of electron-hole pairs generated in the semiconductor can be calculated fromThe short-circuit current generated by electron-hole pairs can be calculated from:33:VPV is the applied cell voltage, kb is the Boltzmann constant, TPV is the temperature of the PV cell and n is the ideality factor .Thus, according to the standard diode equation, the current in a single-junction solar cell under solar illumination is given byI0 is the dark saturation current, the limit of which is a function of the bandgap energy34:Eg is the bandgap energy of the PV cell in units of eV and where the thermodynamic limit of I00 is given by Kiess and Rehwald34:In the expression above , I0 is Based on the above, the electrical efficiency of the ideal PV cell can be determined from:Furthermore, the solar energy absorbed by the thermal absorber is given by:Eth is known, the thermal efficiency of the thermal absorber can be determined from:Eloss is the heat loss, including convective and radiative losses from the thermal absorber to the ambient environment, and ηth,loss is the ratio of the heat loss to the total incident solar energy.Once 47. The extreme PV efficiency value  was identified via a first derivative algorithm, whereas the optimal lower and upper bounds of the optical filter and the optimal PV bandgap were identified via an enumeration algorithm.An in-house code was developed to solve the above set of equations. The one-dimensional integrals were solved by a numerical integration method based on Riemann sums48. The electrical efficiency limits predicted in our study agree well with the S-Q limit and the recent result from Meillaud et al.33, as shown in Fig. The PV model is an important element of the present framework. The electrical efficiency limits of different single-junction solar cells under one sun are calculated using the above model and numerical methodology, and then compared to results from other authoritative publications to validate the model in our study. The well-known S-Q limit has been widely used to estimate the electrical efficiency limits of single-junction solar cells


Solid particles immersed in a fluid can be found in many engineering, environmental or medical fields. Applications are suspensions, sedimentation processes or procedural processes in the production of medication, food or construction materials. While homogenized behavior of these applications is well understood, contributions in the field of pore-scale fully resolved numerical simulations with non-spherical particles are rare. Using Smoothed Particle Hydrodynamics (SPH) as a simulation framework, we therefore present a modeling approach for Direct Numerical Simulations (DNS) of single-phase fluid containing non-spherically formed solid aggregates. Notable and discussed model specifications are the surface-coupled fluid–solid interaction forces as well as the contact forces between solid aggregates. The focus of this contribution is the numerical modeling approach and its implementation in SPH. Since SPH presents a fully resolved approach, the construction of arbitrary shaped particles is conveniently realizable. After validating our model for single non-spherical particles, we therefore investigate the motion of solid bodies in a Newtonian fluid and their interaction with the surrounding fluid and with other solid bodies by analyzing velocity fields of shear flow with respect to hydromechanical and contact forces. Results show a dependency of the motion and interaction of solid particles on their form and orientation. While spherical particles move to the centerline region, ellipsoidal particles move and rotate due to vortex formation in the fluid flow in between. Solid bodies immersed in Newtonian fluids can be found in many fields of mechanical, civil and environmental engineering. Coarse-graining and therefore simplifying the multi-phase response of the mixture, suspensions are often constitutively described on a continuum-scale as non-Newtonian fluids with either shear-thinning or shear-thickening behavior ,2. Such Various numerical methods to investigate suspended particles flow in Computational Fluid Dynamics (CFD) exist. Often either a fully or coupled Discrete Element Method (DEM) is used. Thus contributions exhibit Lattice-Bolzmann-DEM approaches , SmootheOMD-blue ,22 whichOMD-blue . A quiteOMD-blue ,24,25. TConsidering solid particle motion in a fluid, suspensions are one possible application. In this case, the Péclet number which represents the ratio of mechanical to Brownian forces characterizes non-colloidal suspensions ,27. SimuSimilar to Tanner ,28 and VN or, in case of a homogeneous particle discretization, by using the volume In general, a suspension can be discretized as a Newtonian or a non-Newtonian carrier fluid with immersed solid particles influencing the flow behavior . The parConsidering small (suspension-like) systems with only a few number of solid particles and therefore with a particle volume fraction Suspensions consist of a carrier fluid with immersed solid particles. Using fully resolved numerical simulations, hydromechanical forces are evaluated for both, the solid and the fluid phase, individually. Taking into account solid–solid contact forces as well, a coupled formulation of the momentum conservation including an extra part for solid–solid contact is used. Underlying conservation equations are the balance of mass and the balance of momentum, i.e., the Navier–Stokes equations for quasi-incompressible single-phase bulk fluid flow in local formp can be computed as a function of the fluid density c the  speed of sound and Since the fluid phase is considered to be barotropic, the fluid pressure ble flow ,31.When considering solid particles immersed in the fluid phase, underlying conservation equations (Equations  and 3)))3)) are s is the distance between the surfaces of solid bodies This commonly used constitutive equation for solid–solid interactions computes a force acting alongside the vector Equation ) which tFor the purpose of studying the effects of heterogeneous particles in a single-phase fluid, we employ Direct Numerical Simulations (DNS). Our method of choice is Smoothed Particle Hydrodynamics (SPH) ,34 usingThe numerical implementation and therefore discretization of introduced equations results in four cases of SPH particle interaction, which will be dealt with further below: A. Fluid particle with fluid particle, B. Fluid particle with a non-moving solid particle, C. fluid particle with a moving solid particle and D. Moving solid particle with moving solid particle.In SPH , the disW with compact-support and smoothing length h  can be The shown discretization applies to discrete integration points tized by (14)FijVIn Equations (14) and 15), the particle number density , the parAfter introducing the discretization for single-phase fluid flow, the solid–fluid interactions (case C) will be derived for the usage of SPH. Every solid particle aces Γfs .Doing so, earlier introduced particle forces (Equation ) and a tViscous and pressure interaction forces, mass see . Using tThe implementation generally provides no-slip and no-penetration boundary conditions at all fluid–solid interfaces While simulating solid particles immersed in a fluid, also solid bodies Equation . The repEquation  taking iEquation ) is updaMoreover the computation of the total force Equation .As already mentioned, the presented SPH approach is implemented using the HOOMD-blue ,22 librai for the The implementation was validated and underwent a scalability study for a variety of representative test cases for single phase flow in porous media running on massive parallel CPU- and GPU-clusters . Figure κh=3.4dx , where dEquation  . Using This benchmark should be the basis of the validation of the presented SPH approach for suspended objects. In contrast to Jeffery’s original assumption, we choose a small but finite Reynolds number in our numerical simulation, cf. Details of the simulation input as the aspect ratio, initial orientation  from Eqa and b we observe a rotational motion as well (see z) of a surface point normalized by the channel height h was plotted over the time t. As solid particles were not placed right in the center of the channel, the particles move translatoric as well. For cases well see . Since JAdditionally, simulations with initial states where the a-axis of the spheroid is aligned to the tion see . This coc). A detailed investigation of the Reynolds number-dependent motion of particles would be computationally challenging for Re < 1 regarding to the explicit nature of the presented SPH code.Aim of this section was to show first numerical results which are comparable to low-Reynolds number solutions validating the proposed numerical model. Due to the finite size of the computational domain, the chosen finite Reynolds number and the restricted computational time, we could not expect to get limit values of Jeffery. Nevertheless, we show a good agreement with Jeffery’s solution for the simulation with the lowest Reynolds number  of fluid flow containing various solid particles in a channel as shown in In order to investigate the effect of particle-particle interactions, we discuss the near-field solution of solid particles, e.g., of the velocity field of the fluid phase, and discuss the influence of the shape of particles as well as of the presence of other particles (with and without contact). The investigated BVPs present examples for a first numerical analysis of hydromechanical forces and contact forces. i.e., how does fluid flow and solid–solid contact influence the particle motion and vice versa.The simulation domain consists of the fluid sub-domain which is periodic in Since we are interested in investigating the flow behavior dependent on a small number of immersed and interacting particles, we perform simulations with various numbers of solid aggregates (two or four solid bodies) as well as with various aggregate forms . As presented in The simulation results are analyzed in terms of resulting (steady state) velocities and motion of the solid bodies. Additional to a whole domain analysis, we consider fluid flow around solid bodies more closely and discuss influence of hydromechanical forces and contact forces.ar rates ,7.For an better overview, results of spheres and ellipsoids are considered in seperate plots  of solid bodies is considered and analyzed. In eriments ) become eriments ,25.Even in systems with a low number of solid bodies, particle collisions occur. However, the influence of solid–solid contact is attenuated by hydromechanical forces in lubrication layers between the solid particles. Since in SPH a small layer of fluid SPH particles is mostly available between solid particles, a direct particle-particle contact does not occur. This was the main reason for choosing a repulsive force model, where the contact force scales with the distance. Therefore, an influence of hydromechanical forces during the simulation containing four ellipsoidal particles and the influence of contact forces during the simulation containing four spherical solids is analyzed.Additional to hydromechanical forces, simulations with four spherical aggregates show the impact of contact between two solid particles. In The current contribution presents a mesh-less Lagrangian approach aiming for Direct Numerical Simulations of the motion of immersed solid particles (grains) in a Newtonian carrier fluid. Underlying conservation equations for linear momentum of single phase flow were derived and a contact force was included into the model. In contrast to established DEM-CFD models, the fluid–solid momentum exchange is captured via interfacial forces between the solid and the fluid SPH particles. The model was implemented in an explicit quasi-incompressible SPH scheme and validated in terms of periodic motion of single non-spherical solid particles in shear flow at moderate Reynolds numbers. Boundary value problems of spherical and ellipsoidal solid particles were investigated. Resulting velocity profiles of dominating velocity Concentrating on microhydrodynamical effects, we simulate only a small number of immersed particles. Well known effects, like the movement of solid aggregates towards regions with lower shear rates, reported by Chun  and ShauTo come closer to technical applications, like for example concrete pumping processes, we will investigate larger-scale BVPs, where fluid-flow is induced by a volumetrical force corresponding to a pressure difference. Interesting details are the migration of particles dependent on their form and size as observed by Fataei et al.  as well


Radiomic risk models are usually based on imaging features, which are extracted from the entire gross tumour volume (GTVp = 0.10). After additional prospective validation, the consideration of tumour sub-volumes may be a promising way to improve prognostic radiomic risk models.Imaging features for radiomic analyses are commonly calculated from the entire gross tumour volume (GTV The individualisation of radiation oncology is a major objective in modern cancer therapy . Radiomientire) ,3,4,5. Sentire) . Such spentire) . Some ofISO-PET) ,10,11,12ISO-PET) ,14. In tISO-PET) . AlsahafISO-PET)  showed tThus far, only few studies have investigated and analysed specific tumour sub-volumes for radiomic risk modelling. Recently, Algohary et al.  showed tu et al.  identifiAside from these initial findings, in most of the previously described studies, only individual clinical parameters or radiomic features  were investigated. Therefore, systematic investigations of the potential of radiomic risk models based on different tumour sub-volumes are still sparse.In the present study, we systematically compared radiomic models based on two different sub-volumes of the GTV imaging ,21. A mut volume .A retrospective multi-centre cohort consisting of 291 patients with histologically confirmed loco-regionally advanced HNSCC was used. All patients received primary radio-chemotherapy (RCT) and underwent a CT scan with or without contrast-enhancement for treatment-planning purpose. The multi-centre cohort was divided into an exploratory and a validation cohort by an approximate ratio of 2:1. In the exploratory cohort, 149 of the 206 patients were treated in one of the six Partner Sites of the German Cancer Consortium Radiation Oncology Group (DKTK-ROG) between 2005 and 2011 . The remRadiomic risk models were developed to predict the primary clinical endpoint LRC, which was defined as the time from the first day of RCT to the date of loco-regional recurrence (event) or to the end of follow-up (censoring). Ethical approval for the multi-centre retrospective analyses of clinical and imaging data was obtained from the Ethics Committee at the Technische Universität Dresden . All analyses were carried out in accordance with the relevant guidelines and regulations. Informed consent was obtained from all patients.The analysis was divided into two subsequent steps, which are shown in  cohorts ,24.Based on the delineated GTVNine additional images were created by applying spatial filtering to the base image to emphasise image characteristics, such as edges and blobs. Eight additional images were created by applying a stationary coiflet-1 wavelet high-/low-pass filter along each of the three spatial dimensions. One further image was created by applying a Laplacian of Gaussian (LoG) filter consisting of five different filter kernel widths . Subsequently, the tumour mask was re-segmented to include only soft tissue voxels between −150 and 180 Hounsfield units, thereby removing voxels containing air or bone, which may affect feature expression. Features were implemented in compliance with the Image Biomarker Standardisation Initiative . A totalRadiomic risk models were developed using an end-to-end modelling framework, which consists of five steps: (I) feature pre-processing; (II) feature selection; (III) hyper-parameter optimisation; (IV) model development; and (V) model validation. The risk models were generated as previously described . BrieflyCombinations of five feature selection methods and six learning algorithms were used for model development to reduce the risk of incidental findings, based on the recommendation in Leger et al. . The fol(I) The prognostic performance of the radiomic models was assessed on the exploratory and on the independent validation cohort using the concordance index (C-index) ,27. The The median C-indices over all combination of feature selection methods and machine learning algorithms were determined based on the exploratory and the validation cohort for each tumour sub-volume. For the considered feature selection methods and machine learning algorithms, the model performance was statistically compared between the GTVp-values < 0.05 were considered to be statistically significant.(II) Risk-based patient stratification into groups at low and high risk of loco-regional recurrence was performed for each tumour sub-volume and for all model combinations. The results for the selected models (a)–(d) were analysed in more detail. Patients were stratified into a low and high risk group based on the predicted risk of the radiomics models. The cut-off value used for stratification was based on the median predicted risk (median(III) Radiomic signatures were analysed in detail for the models trained on the different selected tumour sub-volumes (a)–(d). Features included in the signatures and their expression values were depicted as heatmaps for the exploratory and the validation cohort. For this purpose, all patients were sorted according to their predicted risk and to their risk group stratification. To quantify the overall importance of the identified features, univariate significance of the individual radiomic features included in the signatures were tested by the Cox model on the entire patient cohort.p = 0.26). The median follow-up time was 21.2 months (range: 1.2–131.9 months) for the exploratory and 24.3 months (range: 1.3–107.2 months) for the validation cohort (p = 0.64). The median GTVp = 0.067, p = 0.066 and p = 0.081, respectively).The number of loco-regional recurrences was 84 for the exploratory and 28 for the independent validation cohort, respectively. The primary endpoint LRC showed no significant difference between both cohorts (p = 0.10), while the difference between GTVp = 0.50). The median performances of the sub-group analyses showed similar results for small GTVRadiomic models were developed and validated based on the different (sub-)volumes of the tumour. Their performance for the prognosis of LRC is summarised in The C-indices of the representative models for each tumour sub-volume are shown in The GTVp-values of the log-rank test for LRC for all representative models on the validation cohort based on the medianp = 0.012). A slightly improved stratification could be achieved by the GTVp = 0.005 and p = 0.006, respectively) as well as by the extended volume GTVp < 0.001). Stratification based on the predicted risk of the corresponding GTVp = 0.11).Patients were stratified into groups at low and high risk for loco-regional recurrence based on the model prediction of the exploratory cohort. p-values for all considered sub-volumes and developed radiomic risk models on the validation cohort are summarised in Radiomics signatures were investigated for the representative models based on GTVThe developed signatures for the different models (a)–(d) consist of two to ten imaging features extracted from the original and wavelet transformed images. The selected features typically comprise first-order statistical or texture-based features. For instance, the ’statistics energy’ feature, which describes the overall density of the tumour volume, appears in all four signatures as a single or as a feature within a cluster . Furtherp = 0.10), i.e., the presented findings require additional validation in the future.Tumours may contain biologically complex structures and exhibit substantial spatial variation. Thus, the main objective of this study was to compare radiomic models based on different sub-volumes of the tumour, i.e., on the tumour rim, the tumour core and the macroscopic tumour extensions, in order to identify potential regions containing the most relevant prognostic information for LRC. Using CT imaging of patients with locally advanced HNSCC revealed that radiomic risk models based on tumour rim sub-volumes achieved a slightly improved prognostic performance and better patient stratification compared to models based on the corresponding core regions. Furthermore, sub-group analyses showed that the differences in prognostic performance between rim and core regions were larger for large tumours compared to small tumours. In general, our analysis showed a good median performance and a better patient stratification for the models based on the GTVThese results are in-line with previously published data of other tumour entities ,11,29. Fe et al.  showed tThe biological characteristics of the tumour rim were already discussed by published data from the Danish Head and Neck Cancer (DAHANCA) group . Based oDefining the precise extent of the macroscopic tumour prior to and during RCT is difficult, especially using CT imaging without contrast enhancement . Slight e future ,36,37.The presented study is motivated form the assumption that hypoxic or necrotic regions preferantly appear in the tumour core due to inadequate vascular supply, and that proliferating cancer cells mainly occur in the tumour periphery . Our retp < 0.01). These parameters revealed C-indices between 0.62 and 0.63 in the validation cohort using univariable Cox regression model. This was slightly lower than observed for the presented radiomic models based on the GTVIn addition to radiomic features, clinical parameters may be relevant for the prediction of treatment outcome. On our cohort, from the parameters shown in One limitation of this retrospective study is the different distribution of the clinical characteristics between the exploratory and validation cohort, e.g., in tumour site and UICC stage . DespiteIn the present study, we showed that radiomic models based on the rim of locally advanced HNSCC achieved a slightly higher prognostic performance for LRC after primary radio-chemotherapy compared to models using the tumour core. This supports our initial hypothesis that the tumour rim is biologically more diverse and important treatment-related processes occur primarily in this region, which may be visible in clinical imaging data. Therefore, after additional prospective validation the consideration of tumour sub-volumes may be a promising way to improve prognostic radiomic risk models.


Protamine sulfate is a common reversal agent of systemic heparinization used during procedures. While the exact epidemiology of adverse events is unknown, prior allergic response to protamine-containing compounds or concomitant use of neutral protamine Hagedorn (NPH) insulin is associated with an increased risk of tachyarrhythmias and bradyarrhythmias. We present a case of a 68-year-old woman with no prior history of protamine sulfate intolerance that suffered bradycardic arrest following protamine infusion.Healthcare providers should recognize the potential for life-threatening tachyarrhythmias and bradyarrhythmias following protamine reversal, especially in diabetic patients at risk for autonomic dysfunction; medication and allergy review are encouraged prior to heparin reversal, especially in diabetic patients. Protamine sulfate is a cationic polypeptide that binds to negatively charged unfractionated heparin and can be used to decrease the risk of bleeding during procedures in patients with systemic heparinization . Though A 68-year-old female with a past medical history of hypertension, peripheral artery disease, and type 2 diabetes complicated by peripheral neuropathy underwent a peripheral angiographic intervention on an occluded left femoral arterial stent, after which she received 20 mg of protamine. During the postoperative period, she experienced bradycardia and hypotension progressing to cardiac arrest. Cardiac monitoring at the time indicated no ventricular arrhythmias. Advanced cardiac life support (ACLS) was initiated and the patient received three rounds of epinephrine prior to transport to the emergency department (ED).In the ED, initial evaluation demonstrated agonal cardiac activity on cardiac ultrasound. The patient received three more rounds of epinephrine, two amps of bicarbonate, and one gram of calcium chloride. The initial cardiac monitoring demonstrated pulseless electrical activity, which progressed to ventricular fibrillation. Defibrillation was performed, resulting in the return of spontaneous circulation (ROSC).The patient was admitted to the medical intensive care unit. She was determined to be neurologically intact and targeted temperature management was not initiated. Medical history was notable for insulin NPH use. She had no prior allergic reaction to NPH, exposure to protamine sulfate, or history of food allergies.Labs showed a rise and fall in troponin I with a peak value of 6.21 mcg/L (reference range ≤0.030 mcg/L). Initial electrocardiogram (ECG) after ROSC showed diffuse ST depression suggestive of subendocardial ischemia  and potential medication reactions. Healthcare providers should recognize the potential for bradyarrhythmias and tachyarrhythmias before protamine administration, particularly in diabetic patients.


In recent years, it has been demonstrated that extracellular vesicles (EVs) can be released by almost all cell types, and detected in most body fluids. In the tumour microenvironment (TME), EVs serve as a transport medium for lipids, proteins, and nucleic acids. EVs participate in various steps involved in the development and progression of malignant tumours by initiating or suppressing various signalling pathways in recipient cells. Although tumour-derived EVs (T-EVs) are known for orchestrating tumour progression via systemic pathways, EVs from non-malignant cells (nmEVs) also contribute substantially to malignant tumour development. Tumour cells and non-malignant cells typically communicate with each other, both determining the progress of the disease. In this review, we summarise the features of both T-EVs and nmEVs, tumour progression, metastasis, and EV-mediated chemoresistance in the TME. The physiological and pathological effects involved include but are not limited to angiogenesis, epithelial–mesenchymal transition (EMT), extracellular matrix (ECM) remodelling, and immune escape. We discuss potential future directions of the clinical application of EVs, including diagnosis (as non-invasive biomarkers via liquid biopsy) and therapeutic treatment. This may include disrupting EV biogenesis and function, thus utilising the features of EVs to repurpose them as a therapeutic tool in immunotherapy and drug delivery systems. We also discuss the overall findings of current studies, identify some outstanding issues requiring resolution, and propose some potential directions for future research.Video abstractSupplementary information accompanies this paper at 10.1186/s12964-020-00643-5. Extracellular vesicles (EVs) are small cell-derived membranous structures, serving as conduits for exchange of significant information between cells , 2. The EVs have been demonstrated to take part in managing tumour spread and medication resistance , 5. TumoAside from the relatively well-researched proteins, lipids, mRNAs, and miRNAs, emerging evidence demonstrates that LncRNAs and circRNAs participate in managing the microenvironment and tumour progression –12. CircIn this literature review, we provide a brief introduction to EVs and the tumour microenvironment (TME), present findings on the influences of T-EVs on neighbouring cells and the TME, and describe EVs from non-malignant cells (nmEVs) and their influences on the TME. We explore the functions of EVs which are potentially important for the next generation of diagnosis and therapy in the field of malignant tumours, discuss the breakthroughs and shortcomings of current research, and suggest possible future directions of research in this field.The malignant properties of tumours and their advancement are not solely regulated by the tumour cells , but alsDuring the onset of tumourigenesis, the microenvironment presents anti-tumour immunity and moderates tumour growth , but as The exchange of information in the TME may influence tumour incidence and advancement, in addition to intrusion, metastasis, and various other malignant biological actions . To explIt has been determined that EVs are intercellular messengers , 26, 27.Theoretically speaking, EVs can be classified  as eitheHowever, this classification system is known to be confusing. The definitions of the terms Exos or MVs in the classification criteria are based on EV biogenesis, but in many studies, researchers use these terms based on particle size distributions rather than their true biogenesis. Top-level researchers in the field of EV research, including Clotilde Thery, indicated that despite the fact that Exos and MVs have disparate biogenic mechanisms, the current technology for EV isolation is not able to precisely subdivide these EV sub-populations . SmallerExos biogenesis consists of a sequence of cellular activities Fig. a. The doMV biogenesis differs from that of MVB-derived Exos , 35, 36.EVs manage many different cellular procedures, such as cell proliferation, survival, and transformation via autocrine and paracrine intercommunication , 37. It There are several procedures through which EVs and their consignments could be transmitted to recipient cells. EVs can dock at the plasma membrane of a target cell , 39, andTypical functions of EVs include promoting development and growth , 41 and The fast growth of the field of EV study has illuminated unfamiliar mechanisms involving the innate intercellular correspondence systems occurring during malignant tumour commencement and progression , 49. NevT-EVs are able to transmit oncogenic molecules between tumour cells Fig. . Glioma In hepatocellular carcinoma (HCC) invasive cell lines, in vitro and in vivo resistance to Sorafenib is caused by the distribution of hepatocyte growth factor (HGF), with the assistance of T-EVs and the subsequent activation of the HGF/c-MET/PI3K/AKT signalling pathway . AdditioMalignant tumour cells are able to transfer resistance via horizontal transmission of T-EVs containing “drug outflow pumps” . AmongstThe existence of selective P-gp/MDR-1 mRNA in T-EVs discharged from doxorubicin-resistant osteosarcoma cells suggests that resistant tumour cells employ methods for spreading drug resistance to sensitive cells. This may occur via delivery of MDR proteins directly to sensitive cells or by delivering the mRNA which encodes them .LncRNA also plays an important role in this process. Lnc-ARSR is strongly expressed in sunitinib-resistant renal cell cancer (RCC) cells compared to sunitinib-sensitive RCC cells. EV-carrying Lnc-ARSR competitively binds miR-34 and miR-449, triggering the improved expression of AXL/c-MET and re-activation of STAT3, AKT, and ERK signalling. Triggered AKT causes the transcriptional de-repression of Lnc-ARSR via destruction of FOXO1/FOXO3a, developing a positive feedback loop . T-EVs cCirc-PDE8A was found to be a highly-expressed circRNA in pancreatic ductal adenocarcinoma (PDAC) –70. CircAngiogenesis is the formation of new blood vessels from pre-existing vessels under particular physiological circumstances, including development or in response to tissue damage . In healThe upregulation of heparanase in myeloma and breast cancer cells is connected with enhanced release of syndecan-1, vascular endothelial growth factor (VEGF), and HGF in T-EVs. This results in increased endothelial infiltration via the extracellular matrix (ECM), and hence enhanced angiogenic activity . T-EVs gT-EVs regulate angiogenesis in tumours through the release of non-coding RNAs. For instance, T-EVs transporting miR-9 stimulate EC migration and tumour angiogenesis via the reduction of suppressor of cytokine signalling 5 (SOCS5) levels and activation of the JAK/STAT pathway . FurtherVascular leakiness is a characteristic of PMN formation , 85. T-EMore research is required to accurately identify the mechanism by which T-EVs influence the stability of the endothelial barrier, as well as the specificity of this particular targeting within the vasculature of various body organs.Tumour-associated FBs ) comprise a large part of the responsive tumour stroma, and carry out essential functions in tumour development. These cells are reprogrammed stromal cells that play a role in malignant tumour initiation, ECM remodelling and advancement, PMN development, and metastasis , 93. In T-EVs deliver EMMPRIN to FBs, causing the production of MMPs and allowing tumour invasion and metastasis . T-EVs cT-EVs, but not those released by non-malignant cells , contain crucial enzymes associated with miRNA biogenesis. These enable cell-independent miRNA maturation inside EVs . InhibitCompared to normal FBs, ovarian tumour-adjacent tFBs constantly downregulate miR-31 and miR-214 while upregulating miR-155. Transfecting miRNA mimics (miR-31 and miR-214 mimics) and miRNA inhibitors (miR-155 inhibitors) induces a functional shift of normal FBs into tFBs, while the reverse transfection causes the opposite result, the reversion of tFBs into normal FBs . SuccessT-EVs are able to stimulate MSCs to differentiate into tumour-supportive cells Fig.  by delivIn numerous cell types, epithelial–mesenchymal transformation (EMT) pertains to tumour intrusion and metastasis , 107. EpT-EVs also take part in EMT Fig. . EVs isoTwo miRNAs (miR-191 and let-7a) have been shown to contribute to melanoma cell-derived T-EV-mediated EMT . A colleSNAI1, TWIST1, ZEB1, ZO1, MMP1, LAMB3, and LAMC2. Utilising shHOTAIR in a pair of human bladder cancer cell lines showed that expression of the master regulator of EMT (SNAI1) was dramatically decreased [Primary urothelial bladder cancer (UBC) cells were determined to affect the expression of EMT genes by means of EV-carrying LncRNA-HOTAIR. These include ecreased . LncRNA-ecreased .A recent study determined that circRNA-circPRMT5 was upregulated in serum and urine EVs from UBC patients. Further investigation determined that circPRMT5 supports the UBC cell EMT by serving as a miR-30c “sponge”. As a result, the expression of its own target gene SNAIL1 and E-cadherin is enriched, allowing the cells to become more invasive .It is commonly acknowledged that metastatic growth is associated with the risk of thrombotic issues, which is a major cause of death in malignant tumour patients . CoagulaEVs associated with coagulation can originate from platelets, inflammatory cells, and malignant tumour cells . Raised Fascinatingly, mutated KRAS and TP53 are associated with raised levels of TFs in T-EVs secreted by human colorectal tumour cells . PancreaThe TME is penetrated by a range of immune cells, including lymphocytes  cells, and T regulatory (Treg) cells), dendritic cells (DCs), monocytes, Mφs, myeloid-derived suppressor cells (MDSCs), and granulocytes . The major function of these cells is to ensure immune supervision. Nevertheless, tumour cells are efficient in regulating signalling pathways within these immune cells, turning them into an immunosuppressive entity and resulting in improved malignant tumour cell survival and proliferation . There iOne study determined that T-EVs induce immunosuppression by promoting apoptosis of hematopoietic stem cells (HSCs), DCs, and peripheral blood lymphocytes (PBLCs) . Many T-T-EVs carry TGF-β externally and transport it to T cells, suppressing their proliferation in response to IL-2 and altering their phenotype to Treg cells , 135. FuThese T-EVs have been revealed to trigger DCs and cause IL-6 secretion, which enhances tumour invasion by increasing MMP-9 metalloproteinase expression . T-EVs aA previous study confirmed that PD-L1 exists in EVs derived from the urine or blood of patients with early IgA nephropathy . ResearcTumour-released miRNAs have similarly been associated with immunosuppression. For example, miR-214 carried by T-EVs was effectively transported into recipient T cells. An in vivo study has shown that miR-214 mediates Treg cell expansion, causing increased immunosuppression and tumour growth in mice .Mφs are multifunctional antigen-presenting cells characteristically classified into a pair of polarised phenotypes: pro-inflammatory (M1) and anti-inflammatory (M2) . Tumour-Throughout the process of malignant tumour progression, the molecular and cellular environments of stromal cells and their extracellular proteins and enzymes are dynamically changing as a result of the influence of T-EVs Fig. . ECM remResearch has revealed that EVs participate in invasion and metastasis by means of invadopodium formation , 158. InEVs could be used as carriers by malignant tumour cells to promote drug resistance via drug sequestration and banishment Fig. . SheddenMalignant tumour cells can also make use of EVs as “fake targets”, thus weakening targeted treatments Fig. . T-EVs tThis is exemplified by B-cell lymphoma, when the existence of CD20 on the surface of EVs protects targeted lymphoma cells from rituximab (an anti-CD20 mAB) . Both inImmune checkpoint blockade therapies feature anti-CTLA-4 monoclonal antibody (mAb), anti-PD-1 mAb, and anti-PD-L1 mAb . It is lMetastasis is a multi-step procedure resulting in the spread of primary tumour cells to distant body organs. T-EVs have been associated with all steps of tumour invasion and metastasis , 170–172T-EVs possess their own protein “postal code” of specific integrin profiles Fig. b. This dCircRNAs in blood EVs, called ciRS-133, are closely associated with the light browning of white adipose tissue (WAT) and malignant tumour-associated cachexia. After being provided to pre-adipocytes, ciRS-133 reduce miR-133 expression, activate PRDM16, and promote the differentiation of preadipocytes into brown-like cells. It has been demonstrated that ciRS-133 KD may prevent tumour-implanted mice from struggling with malignant tumour-related cachexia, demonstrating the contribution of EV-circRNAs in tumour pathogenesis .Tumour expansion and drug resistance are not only decided by malignant tumour cells but are also sustained by non-tumour cells inside the TME. Hence, it is quite reasonable to think that nmEVs also play an important role in affecting the TME Fig. . Thus tFMSC-derived EVs (MSC-EVs) may generate drug resistance in gastric cancer cells by activating the CaM-Ks/Raf/MEK/ERK signalling pathway . EVs carHRAS overexpression in EpiCs boosts the packing of mesenchymal markers (including vimentin and MMPs) in EVs, possibly causing EMT in recipient cells .Transfer of miR-365 in Mφ-derived EVs (Mφ-EVs) causes pancreatic adenocarcinoma cells to become resistant to gemcitabine in vitro and in vivo . M2-Mφs The transfer of miRNAs specifically targeting PTEN expression from astrocyte-derived EVs to invading tumour cells in the brain microenvironment supports brain metastasis, despite the fact that other autocrine and paracrine signalling may also be coordinated throughout tumour development .CircRNAs are commonly expressed in individual cells such as blood cells , 186. ReGiven that they are incredibly stable, abundant, and tumour-specific, T-EVs have numerous unique advantages as biomarkers . One intEV integrins (as a counterpart to tumour-expressed integrins) can act as biomarkers to predict the probability of malignant tumours, in addition to determining metastatic tendencies in specific organ sites . SpecifiCirculating T-EVs from patients with stage IV melanoma carry a protein signature composed of the melanoma-specific protein tyrosinase-related protein-2 (TYRP2), very late antigen 4 (VLA-4), HSP70, and MET oncoprotein . T-EVs fResearch has illustrated that the detection of PD-L1(+) EVs in serum is correlated with poor prognosis in individuals with pancreatic ductal adenocarcinoma . New finAdditionally, EVs transport single-stranded DNA (ssDNA), which summarises genomic eccentricities such as oncogene amplifications (such as MYC) in the primary tumour . In the Tumour-specific mRNA isolated from T-EVs from the serum and tissue of glioblastoma patients reflects the mutational condition of EGFRvIII , 201. MiLncRNAs associated with T-EVs are also appealing as prospective biomarkers. Nevertheless, LncRNAs in EVs may work as biomarkers in various other malignant tumours, such as LncRNA-p21 in prostate cancer and LncRNA-HOTAIR in bladder cancer . In coloNonetheless, the features of circRNAs mark these molecules as a better option for detecting disorders due to their closed conformation and resistance to RNase. Compared to the 48-h half-life of the majority of circRNAs, the ordinary half-life of miRNAs is normally less than 10 h . ScientiWith recent technological improvements, microfluidic technology has been introduced into the field of EV study. Compared with traditional methods, microfluidic technology can carry out EV-based diagnosis more easily, efficiently, and economically .EV biogenesis is a major target for EV-targeting therapy in malignant tumour treatment –219. A n. In a mouse xenograft model of melanoma, they demonstrated that therapy with a proton pump inhibitor reduces the release of EVs and enhances tumour cell sensitivity to cisplatin [Federici et al. carried out therapy with a proton pump inhibitor to observe the effects of both cisplatin uptake and EV release in vitro and in vivoisplatin . Numerouisplatin , preventisplatin . Inhibitisplatin .While many of the agents specifically blocking T-EV release from malignant tumours lack specificity, some inhibitors target tumour-specific enzyme isoforms. This is the case for peptidylarginine deiminase (PAD)2 and PAD4 inhibitors, which are overexpressed in prostate and ovarian malignant tumour cells. Their inhibition by chloramidine minimises T-EV production, thus increasing the sensitivity of malignant tumour cells to chemotherapy drugs . In a moAlthough these treatments have had success in vitro and sometimes in vivo, their lack of selectivity for malignant tumour cells restricts their therapeutic usage. This is not the case for the specific elimination of circulating T-EVs from plasma.In a technique quite similar to haemodialysis, extracorporeal hemofiltration with cartridges composed of hollow fibres (with a size cut-off of 200 nm) combined with an affinity matrix allows specific elimination of ultra-filtered EVs. This procedure is known as Adaptive Dialysis-like Affinity Platform Technology (ADAPT™), and was first developed by Aethlon Medical Inc. for eliminating Hepatitis C virus (HCV) particles from the bloodstream of contaminated patients . The expActivation of anti-tumour T cell reactions by DC-derived EVs (DC-EVs) has been determined to be critical in reducing the expansion of well-established tumours . LoadingEVs may be therapeutically targeted to supply anti-tumour cargos to malignant cells . Based oCapitalising on EVs, researchers have the ability to target medications to tumour cells. EVs may raise the therapeutic index of doxorubicin (DOX). EVs carrying doxorubicin (EV-DOX) avoid cardiac toxicity by partly restricting the crossing of DOX via myocardial ECs . AnotherAn in vivo study revealed that neuron-targeted EVs packed with Bace1 siRNAs specifically and significantly decreased Bace1 mRNA (60%) and protein (62%) in nerve cells . SimilarThese results suggest that EVs may indeed be beneficial as drug delivery tools. Although several anti-tumour therapies have been investigated/tested in preclinical models and phase I clinical trials, these studies have reinvigorated the desire for novel anti-cancer therapies.In the previous sections, we introduced the role of EVs in the TME, including the role of T-EVs and EVs from non-malignant cells. Some representative contents of EVs, their functions and mechanism are shown in Table The physiological and pathological study of tumours can be mainly categorised into two levels: (1) single molecule studies, which study a specific molecule in EVs and its role; and (2) functional observational studies, which determine the changes and possible roles of EVs and their cargo in the TME. There is a very large gap between the functional observational studies and the single molecule studies, as well as between in vivo and in vitro studies.EVs through their cargo play a variety of different roles, but it is important to determine which of these play primary or secondary roles, to what percentage this function is carried out, and whether there is synergy or antagonism between these molecules or EVs. Current research is not sufficient to provide satisfactory answers. There is also a gap between molecules or single source EVs and the TME. This is because the TME is complex, containing many cells and molecules carrying out different functions. Rather than a single molecule or single source of EVs contributing to their functions, there is an interplay between various molecules and all sources of EVs.Moreover, cellular communication has not been well studied. The interaction between cells is bidirectional, and tumour cells and non-malignant cells in the body continuously interact with each other in dynamic equilibrium to form the TME. Many in vitro experiments are carried out using EVs from one type of cell to stimulate another type of cell. The effect of EVs on cells in the TME is not a simple and direct effect, but rather an effect similar to the “iterative effect”, such that the influence of T-EVs on non-malignant cells can also affect the content of nm-EVs; and the influence of affected nm-EVs on tumours and T-EVs is changed. When this process is continuously repeated, the features of EVs will completely deviate from the simple in vitro model. Although it is not clear what kind of new model will be most appropriate, organ-on-a-chip may be a more appropriate option for future research .Minimal Information for Studies of Extracellular Vesicles 2018 (MISEV2018) endorses EVs as the standard terminology for vesicles which are released naturally by the cell and enclosed within a lipid bilayer without replication capacity  . Over thFinding discriminating markers for T-EVs versus normal stromal EVs has been an important research direction in this field for a long time. Although a number of markers on EVs have been identified for a particular type of tumour , so far, no universal markers have been found. If one or more universal/general markers, which can cover various kinds of tumours not just one or a few, can be found on T-EVs, then the use of T-EVs for tumour diagnosis will become even more meaningful. If no universal markers can be found, then compromise tactics will need to be used. EVs from various tumour sources can be analysed, and the data can be uploaded to a database. Through bioinformatics technology, a minimal set of genes could be found, which can cover as many tumours as possible, and the database should be updated as often as possible.New evidence shows that EV-circRNAs could possess important biological functions in different pathological and physiological procedures. EV-circRNAs are confirmed to be extremely stable . FurtherLiquid biopsy is based on the detection and analysis of biomarkers , cell-free nucleic acids (cfNAs), and EVs) in readily-available body fluids such as peripheral blood –246. TheAnalysis of EVs is a promising potential new star in the field of liquid biopsy, and an approach that has attracted more and more attention in recent years is single-EV analysis, which is of crucial significance for the precise analysis and diagnosis of diseases . A multiEVs are involved in various pathophysiological conditions such as development and progression of disease , 249. SeAlthough many of the EV-based therapeutic approaches performed well in pre-clinical models and phase I clinical trials, they exhibited many notable issues in subsequent phase II clinical trials. In patients with advanced non-small cell lung carcinomas, interferon-γ (IFN-γ)-DC-EV treatment ceased to be effective, revealed by long-term clinical observation . TherefoOn account of their good stability, long circulating half-life and relatively good bio-safety, EVs are considered to be potential drug delivery systems with high delivery efficiency and low toxicity . MoreoveAptamers are RNAs or single-stranded DNAs (ssDNAs) folded into particular 3D structures with high specificity and affinity through a similar mechanism to antigen–antibody binding . AptamerCRISPR/Cas9 treatment through EVs is also a very promising research prospect. At present, this technology is mainly used as a research tool. The most typical application of this treatment is to confirm the spread of EVs as reporters. It would be interesting to compare EV-based CRISPR/Cas9 with known methods using CRISPR/Cas9 for gene therapy, in addition to determining the differences in the scope of application, treatment effects, and the advantages and disadvantages of this approach.EVs function as a transport medium for various molecules in the TME, and therefore have a variety of potential uses in the diagnosis and treatment of cancer. EVs also participate in the progression of various processes involved in malignant tumour development. Tumour cells and non-malignant cells typically communicate with each other, together determining the progress of the disease. Although T-EVs are known for orchestrating tumour advancement via systemic pathways, nmEVs also contribute substantially to malignant tumour development.In this review, we have summarised the features of both T-EVs and nmEVs, and their roles in tumour progression, metastasis, and EV-mediated chemoresistance in the TME. This review discusses recent and current research regarding the clinical applications of EVs, the findings of these studies, and how this information can be used to repurpose EVs as a therapeutic tool. This sound and current overview of the present research, questions to be addressed, and potential directions for future research in the field makes a significant contribution to the literature.