**Elizabeth Ramsey\*, Jorge Pesantez, Mohammad Ali Khaksar Fasaee, Morgan DiCarlo, Jacob Monroe and Emily Zechman Berglund**

Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA<EMAIL_ADDRESS>(J.P<EMAIL_ADDRESS>(M.A.K.F<EMAIL_ADDRESS>(M.D<EMAIL_ADDRESS>(J.M<EMAIL_ADDRESS>(E.Z.B.)

**\*** Correspondence<EMAIL_ADDRESS>
Received: 30 September 2020; Accepted: 29 October 2020; Published: 2 November 2020

**Abstract:** Water availability is increasingly stressed in cities across the world due to population growth, which increases demands, and climate change, which can decrease supply. Novel water markets and water supply paradigms are emerging to address water shortages in the urban environment. This research develops a new peer-to-peer non-potable water market that allows households to capture, use, sell, and buy rainwater within a network of water users. A peer-to-peer non-potable water market, as envisioned in this research, would be enabled by existing and emerging technologies. A dual reticulation system, which circulates non-potable water, serves as the backbone for the water trading network by receiving water from residential rainwater tanks and distributing water to households for irrigation purposes. Prosumer households produce rainwater by using cisterns to collect and store rainwater and household pumps to inject rainwater into the network at sufficiently high pressures. The smart water grid would be enabled through an array of information and communication technologies that provide capabilities for automated and real-time metering of water flow, control of infrastructure, and trading between households. The goal of this manuscript is to explore and test the hydraulic feasibility of a micro-trading system through an agent-based modeling approach. Prosumer households are represented as agents that store rainwater and pump rainwater into the network; consumer households are represented as agents that withdraw water from the network for irrigation demands. An all-pipe hydraulic model is constructed and loosely coupled with the agent-based model to simulate network hydraulics. A set of scenarios are analyzed to explore how micro-trading performs based on the level of irrigation demands that could realistically be met through decentralized trading; pressure and energy requirements at prosumer households; pressure and water quality in the pipe network.

**Keywords:** rainwater harvesting; water trading; dual reticulation; decentralized water supply; water distribution system; agent-based modeling; urban water management; smart city


**INTERACTIVE MULTIMEDIA**

Edited by **Ioannis Deliyannis**

**INTECHOPEN.COM**

http://dx.doi.org/10.5772/2394 Edited by Ioannis Deliyannis

#### **Contributors**

Carlos Eduardo Cirilo, Antonio Francisco Do Prado, Luciana Aparecida Martinez Zaina, Wanderley Lopes de Souza, Sue Fenley, Joao Benedito Dos Santos Junior, Manuela Damiana Santos Almeida Guedes, Pedro Almeida, Kamisah Osman, Tien Tien Lee, Alain Durand, Henryk Krawczyk, Jerzy Proficz, Jorge Montalvo, Massimo Ancona, Davide Conte, Gianluca Quercini, Betty Bronzini, Marina Milovanović, Djurdjica Takaci, Aleksandar Milajic, Michael Henninger, Christina Barth, Mabel Vazquez Briseno, Francisco Iwao Hirata, Juan De Dios Sanchez Lopez, Juan Iván Nieto Hipólito, Elitania Jimenez Garcia, Christian Navarro Cota, Edward Berger, Charles Krousgrill, Bing Zhang, Ioannis Deliyannis

#### **© The Editor(s) and the Author(s) 2012**

The moral rights of the and the author(s) have been asserted.

All rights to the book as a whole are reserved by INTECH. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECH's written permission. Enquiries concerning the use of the book should be directed to INTECH rights and permissions department (permissions@intechopen.com).

Violations are liable to prosecution under the governing Copyright Law.

Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2012 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

Legal deposit, Croatia: National and University Library in Zagreb

Additional hard and PDF copies can be obtained from<EMAIL_ADDRESS>
Interactive Multimedia Edited by Ioannis Deliyannis p. cm. ISBN 978-953-51-0224-3 eBook (PDF) ISBN 978-953-51-5648-2


7. Hypothesis tests of joint significance of the regressions' alphas and regressions

Having presented the methodology and statistical results, in this part, I present an answer to important question if the estimated models can completely capture expected returns. To obtain a more absolute answer to this question GRS f-tests were conducted on results obtained from the first hypothesis' portfolio regressions. The GRS statistic is used to test if the alpha values from regressions are jointly indistinguishable from zero.

If a model completely captures expected returns, the intercept should be indistinguishable from zero. Hence, the first hypotheses are:

H0: α^1 = α^2 = … = α^16 = 0 (the regression alpha is not significantly different from zero).

H1: α^1, α^2, …, α^16 6¼ 0 (the regression alpha is significantly different from zero).

The second hypotheses for all equations are:

excess return of 1.55%. One explanation could be that even if the biggest-sized companies have low profitability, if it is an aggressive investing company and the expectation of the market participants is positive, then a monthly average return of 1.55% would be justified. As you may see in Panel C of Table 3, big companies with aggressive investing have the highest returns.

Factor spanning regressions are a means to test if an explanatory factor can be explained by a combination of other explanatory factors. Spanning tests are performed by regressing returns of one factor against the returns of all other factors and analysing the intercepts from that regression. Table 4 shows regressions for the 5F-FF model's explanatory variables, where four factors explain returns on the fifth. In the RM-Rf regressions, the intercept is not statistically significant (t = 0.55). Regressions to explain HML, RMW and CMA factors are strongly positive. However, regressions to explain SMB show insignificant intercept, with intercept of 0.27% (t = 1.34). These results suggest that removing either the RM-Rf or SMB factor would not hurt the mean-variance-efficient tangency portfolio produced by combining the remaining four

Intercept RM-Rf SMB HML RMW CMA R2

Coefficient 0.45 0.34 0.41 0.29 0.21 0.20

Coefficient 0.27 0.02 0.00 0.07 0.01 0.12

Coefficient 0.55 0.04 0.01 0.06 0.10 0.19

Coefficient 1.06 0.03 0.12 0.06 0.07 0.17

Coefficient 1.04 0.02 0.03 0.11 0.07 0.16

RM-Rf is the equal-weighted return on BIST-100 Index minus the 3-month Tryribor rate. SMB is the size factor, HML is the value factor, RMW is the profitability factor, and CMA is the investment factor. The factors are constructed using individual sorts of stocks into two size groups and three B/M groups, three OP groups or three Inv groups. Bolded and

Table 4. Factor spanning regressions on five factors: spanning regressions using four factors to explain average returns

t-Stat 0.55 1.17 1.81 1.33 1.02

t-Stat 1.34 1.17 0.07 1.32 0.29

t-Stat 2.19 1.81 0.07 0.82 1.50

t-Stat 4.18 1.33 1.32 0.82 1.01

t-Stat 3.95 1.02 0.29 1.50 1.01

shaded t-statistics indicate the significance at the 5% level.

on the fifth (June 2000–May 2017, 204 months).

6. Factor spanning regressions

78 Financial Management from an Emerging Market Perspective

factors.

RM-Rf

SMB

HML

RMW

CMA

H0: α^1 = α^2 = … = α^48 = 0 (the regression alphas are jointly indistinguishable from zero).

H1: α^1, α^2,…, α^48 6¼ 0 (the regression alphas are jointly distinguishable from zero).

Finally, average individual regression alphas and joint GRS regression f-values were used together in order to compare the performance between the tested models.

#### 7.1. The GRS regression equation

The GRS test was developed by Gibbons et al. [24] and serves as a test of mean-variance efficiency between a left-hand-side collection of assets or portfolios and a right-hand-side model or portfolio. The following regression defines the GRS test:

$$f\text{GRS} = \frac{T}{N} \times \frac{T-N-L}{T-L-1} \times \frac{\widehat{\alpha}' \times \widehat{\Sigma}^{-1} \times \widehat{\alpha}}{1 + \overline{\mu}' \times \widehat{\Omega}^{-1} \times \overline{\mu}} \sim F(N, T-N-L) \tag{15}$$

where α^ is a N�1 vector of estimated intercepts, Σb an unbiased estimate of the residual covariance matrix, μ a L�1 vector of the factor portfolios' sample means and Ωb an unbiased estimate of the factor portfolios' covariance matrix.

The GRS test is used in this study to determine whether the alpha values from individual model regressions are jointly non-significant and hence to find out if a model completely captures the sample return variation. As intercepts from individual regressions approach zero, the GRS statistic will also approach zero. However, since the GRS statistic derives its results from comparing the optimal LHS and RHS portfolios, the resulting statistic is not strictly comparable between models.

#### 7.2. Model performance

A set of several summary metrics were deployed in order to compare the performance of the asset pricing models. GRS statistics and average alpha values were used as the main two statistics in order to determine how good the different asset pricing models performed in explaining portfolio returns. In addition to these statistics, average absolute alpha spread was added for a more complete picture of the alpha results. Furthermore, different models' explanatory power was measured using R2 values.

If a capital asset pricing model (CAPM, three-factor model or five-factor model) completely captures expected returns, the intercept (alphas) is indistinguishable from zero in a regression of an asset's excess returns on the model's factor returns.

Table 5 shows the GRS statistics of [24] that tests this hypothesis for combinations of LHS portfolios and factors. The GRS test easily rejects all models considered for all LHS portfolios and RHS factors. The probability, or p-value, of getting a GRS statistic larger than the one observed if the true intercepts are all zero is shown in column 'pGRS'. One can see from Table 5 that except CAPM in Panel A and Panel C, sets of left-hand-side returns, the p-values round to zero to at least three decimals. Only five-factor model in Panel C has a p-value of 0.30, and it is still significant at the 5% level.

The table tests the ability of CAPM, 3F-FF and 5F-FF models to explain monthly excess returns on 16 size-B/M portfolios (Panel A), 16 size-OP portfolios (Panel B), 16 size-Inv portfolios (Panel C) and a joint sample of all 48 portfolios (Panel D). For each panel, the table shows the tested model; the GRS statistic testing whether the expected values of all 16 or 48 intercept estimates are zero; the p-value for the GRS statistic; the average absolute value of the intercepts, Avg |α|; the average absolute deviation from the average intercept; and the average R2 . Bolded and shaded GRS statistics indicate the significance at the 5% level.

Table 5. Summary of statistics for model comparison tests: summary of statistics for tests of CAPM, 3F-FF and 5F-FF models (June 2000–May 2017, 204 months).

Fama and French [19] state that they want to identify the model that is the best (but imperfect) story for average returns on portfolios formed in different ways, and they use absolute value of average alphas (|α|) and two more statistics (for a detailed description of these statistics, see ([21], pp. 10). Following [22], in this study I use only average absolute values of alphas (|α|) and the average absolute deviation from the average intercept (Avg j j α � α ). Both [19, 22] pay specific importance of the absolute values of alphas. Relatively, small values of Avg|α| or Avg j j α � α in equations are regarded as better in identifying the best model. However, as is seen in Table 5, Avg|α| of CAPM model in Panel A has the lowest value, but fGRS and pGRS show that we cannot reject the hypothesis that the regression alpha is significantly different from zero. The same case is valid for the CAPM in Panel C in Table 5. Therefore, I think concentrating on the magnitude of the alpha values or absolute deviation from the average intercept (Avg j j α � α ) in the equations to decide on the best performing model may not be a healthy approach. Keeping this in mind, my main conclusion from the model comparison tests follows.

The GRS test in Panel D clearly rejects the second hypothesis for all models tested. Furthermore, the GRS test in Panel D suggests that the 5F-FF model is the model closest to a complete description of asset returns. Beware that, as discussed in [19], fGRS values between models cannot be strictly compared. Instead, the fGRS is mainly interpreted as a test to reject or accept a model's explanation of returns on a set of left-hand-side portfolios. The fGRS in [21, 22] is used in comparisons between models only in combination with a comparison of average alpha values. Fama and French [19] instead use the numerator of a GRS regression as a comparison value when choosing which factors to include in a model. In every panel of Table 5, explanatory power measured by average R2 clearly improves with the inclusion of more factors. For this reason, metrics for explanatory power are important in providing additional help to compare these types of asset pricing models.

The main conclusions from the performance comparison tests can be summarised as follows. A GRS test on the joint set of all tested portfolios clearly rejects all tested models as complete descriptions of average returns. The CAPM model elicits the lowest average absolute alpha values of the three tested models throughout all tests but shows a statistically insignificant fGRS value compared to other models. Considering all the evidence in Table 5, it is clear that the 5F-FF model shows the strongest performance out of the three models for the sample. In the following section, I provide alpha values from individual left-hand-side portfolio regressions, and I re-examine the alpha values from a different perspective.


3.1.1. Definition of Research Questions

The first stage involves motivating the study and defining the research questions. Blockchain is an ever-evolving technology and is a key part of the digital transformation process for most businesses. Hence, understanding this technology and its impact is important. However, a key part that often gets missed by most prior literature is how blockchain relates to corporate governance, its theories and implications. Furthermore, due to the proliferation of general blockchain adoption literature, it is important to conduct a systematic review to comprehend prior literature and identify insights relevant for blockchain adoption in corporate governance. To this end, the following research questions are formulated.

1: What are the present and future use cases of blockchain applications in corporate governance?

This research question identifies present and future blockchain use cases from a wide range of applications of blockchain across many industries, from general adoption articles and finance-related articles themselves.

2: What are the trends, gaps and similarities between prior industry and academic literature?

#### *Int. J. Financial Stud.* **2020**, *8*, 36

This question is formulated to obtain an overview of over- or under-explored areas and differences of interest between the industry and academic literature with regard to blockchain and corporate governance. The findings related to this research question would aid future researchers and industry practitioners to identify gaps where more research and applications are required and would promote collaborations between industry and academia.

3. What are the implications of adopting blockchain in corporate governance and linking to theories in prior literature?

Clearly identifying the impact of blockchain adoption for corporate governance is key for successful deployment and maintenance of blockchain within the finance industry. By linking existing traditional corporate governance theories to blockchain adoption, this research question aids in better understanding impacts and unintended consequences.

4: What are the advantages, challenges, misconceptions and limitations for blockchain adoption in corporate governance during and post COVID-19?

Due to lockdowns and social distancing, most businesses have been pushed for digital transformation at a faster pace, including those in the financial sector. Blockchain being one of the key fintech technologies, this research question aims to understand the advantages, limitations and barriers of adoption of blockchain in corporate governance during and post COVID-19.

5. What are the links between investments in blockchain internationally and future forecasts?

This empirical research question collates empirical data on blockchain investments from several data sources and forecasts future investments to identify future investment trends.


**3. Sensitivity analysis**

If the model in certain areas has not been used, then it is necessary to carry out sensitivity analysis. Sensitivity analysis limits the number of parameters that need optimization to achieve good correlation between simulated and measured data. The method of analysis in the SWAT model called PARASOL is based on the method of Latin Hypercube One-factorat-a-Time (LH-OAT). LH-OAT combines the advantages of global and local sensitivity analysis (van Griensven et al., 2006). This method performs LH sampling of data at first, followed by OAT sampling. The new scheme allows the LH-OAT to unmistakably link the changes in the output data of each model to the modified parameter (van Griensven et al., 2006). For the sensitivity analysis and calibration a special tool called SWAT-CUP is available which includes all important algorithms (GLUE, PSO, MCMC, PARASOL and SUFI2) of which Sequential Uncertainty Method (SUFI2) was shown to be very effective in identifying sensitive parameters (Abbaspour et al., 2007).

Tool within the model can automatically carry out the sensitivity analysis without the measured data or with the measured data. The tool varies values of each model parameter within a range of (MIN, MAX). Parameters can be multiplied by a value (%), part of the value can be added to the base value, or the parameter value can be replaced by a new value. The final result of the sensitivity analysis are parameters arranged in the ranks, where the parameter with a maximum effect obtains rank 1, and parameter with a minimum effect obtains rank which corresponds to the number of all analyzed parameters. Parameter that has a global rank 1, is categorized as "very important", rank 2 − 6 as "important", rank 7 − 41 (i.e. the number of parameters in the analysis − i.e. flow 7 - 26) as "slightly important" and rank 42 (i.e. flow 27) as "not important" because the model is not sensitive to change in parameter (van Griensven et al., 2006).

Sensitivity analysis was performed using the measured data of the river Reka tributary Kožbanjšček (subcatchment 5) and the river Dragonja (subcatchment 14). The analysis was performed for an average daily flow, sediments, TP and NO3 -. Table 3 represents for each model the first 10 parameters that have the greatest impact on the model when they are changed. The sensitivity analyses demonstrated great importance of the hydrological parameters that are associated with surface and subsurface runoff.

Alpha\_Bf factor determines the share between the base and surface flow contribution to the total river flow. Cn2 curve runoff determines the ratio between the water drained by the surface and subsurface runoff in moist conditions. Ch\_K2 describes the effective hydraulic conductivity of the alluvial river bottom (water losing and gaining). Surlag represents the

*n*

=± − ⋅ (6)

2 ( 1) *s txtn*

If the model in certain areas has not been used, then it is necessary to carry out sensitivity analysis. Sensitivity analysis limits the number of parameters that need optimization to achieve good correlation between simulated and measured data. The method of analysis in the SWAT model called PARASOL is based on the method of Latin Hypercube One-factorat-a-Time (LH-OAT). LH-OAT combines the advantages of global and local sensitivity analysis (van Griensven et al., 2006). This method performs LH sampling of data at first, followed by OAT sampling. The new scheme allows the LH-OAT to unmistakably link the changes in the output data of each model to the modified parameter (van Griensven et al., 2006). For the sensitivity analysis and calibration a special tool called SWAT-CUP is available which includes all important algorithms (GLUE, PSO, MCMC, PARASOL and SUFI2) of which Sequential Uncertainty Method (SUFI2) was shown to be very effective in

Tool within the model can automatically carry out the sensitivity analysis without the measured data or with the measured data. The tool varies values of each model parameter within a range of (MIN, MAX). Parameters can be multiplied by a value (%), part of the value can be added to the base value, or the parameter value can be replaced by a new value. The final result of the sensitivity analysis are parameters arranged in the ranks, where the parameter with a maximum effect obtains rank 1, and parameter with a minimum effect obtains rank which corresponds to the number of all analyzed parameters. Parameter that has a global rank 1, is categorized as "very important", rank 2 − 6 as "important", rank 7 − 41 (i.e. the number of parameters in the analysis − i.e. flow 7 - 26) as "slightly important" and rank 42 (i.e. flow 27) as "not important" because the model is not sensitive to change in

Sensitivity analysis was performed using the measured data of the river Reka tributary Kožbanjšček (subcatchment 5) and the river Dragonja (subcatchment 14). The analysis was performed for an average daily flow, sediments, TP and NO3-. Table 3 represents for each model the first 10 parameters that have the greatest impact on the model when they are changed. The sensitivity analyses demonstrated great importance of the hydrological

Alpha\_Bf factor determines the share between the base and surface flow contribution to the total river flow. Cn2 curve runoff determines the ratio between the water drained by the surface and subsurface runoff in moist conditions. Ch\_K2 describes the effective hydraulic conductivity of the alluvial river bottom (water losing and gaining). Surlag represents the

*x* sample arithmetic mean (alternative scenario)

*s* sample standard deviation (alternative scenario)

identifying sensitive parameters (Abbaspour et al., 2007).

parameters that are associated with surface and subsurface runoff.

parameter (van Griensven et al., 2006).

*n* number of pairs (alternative scenario)

Student distribution

**3. Sensitivity analysis**

2 *t*α

*μ* average of the corresponding random variables (base scenario)

α

surface runoff velocity of the river and Esco describes evaporation from the soil. For the sediment modelling the most important parameters are Spcon and Spexp that affect the movement and separation of the sediment fractions in the channel. Ch\_N − Manning coefficient for channel, determines the sediment transport based on the shape of the channel and type of the river bed material. Ch\_Cov − Channel cover factor and Ch\_Erod − Channel erodibillity factor proved to be important for the Dragonja catchment. Soil erosion is closely related to the surface runoff hydrological processes (Surlag, Cn2). The analysis showed importance of the hydrological parameters that are associated with surface and subsurface runoff (Cn2, Canmx, Sol\_Awc), evaporation (Revapmin, Esco, Blai), base flow (Alpha\_Bf) and groundwater (Rchrg\_Dp, Gwqmn), suggesting numerous routes by which sediment nitrate nitrogen (NO3-N) and TP are transported (Table 3). We noticed that the amount of N is also influenced by other parameters that are not included in the sensitivity analysis tool like Rate factor for humus mineralization of organic nutrients active N and P (CMN.bsn), half-life of nitrates and the shallow aquifer (HLIFE\_NGW.gw), fraction of algal biomass that is N (Al1.wwq). TP results are significantly affected by the parameters that control surface runoff (Cn2, Canmx, Usle\_P). Usle\_P factor adjusts the USLE value for a particular land management. This means that the soil loss from the terraced land is different, from non terraced slopes. Parameters which have a significant impact on P, but not included in the sensitivity analysis tool are: fraction of algal biomass that is P (Al2.wwq), P availability index (PSP.bsn), P enrichment ratio for loading with sediment (ERORGP.hru), BC4.swq, benthic sediment source rate for dissolved P in the reach (RS2.swq), organic P settling rate (RS5.swq).

Table 3. SWAT parameters ranked by the sensitivity analysis for the Reka subcatchment 5 and Dragonja subcatchment 14 (1998 - 2005)


**RESEARCH ASPECTS**

Edited by **Davy Vanden Broeck**

**INTECHOPEN.COM**

### **Human Papillomavirus and Related Diseases - From Bench to Bedside - Research Aspects**

http://dx.doi.org/10.5772/1210 Edited by Davy Vanden Broeck

### **Contributors**

Jacome Bruges-Armas, Isa Dutra, Iris Foroni, Ana Rita Couto Rendeiro, Manuela Lima, Yusuke Hiraku, Ramon Garcia-Escudero, Jesus M Paramio, Agueda Buitrago-Perez, Carlos Perez-Plasencia, Felipe Vaca-Paniagua, Veronica Fragoso-Ontiveros, Omar Vargas-Hernandez, Patricia Piña-Sanchez, Cesar Lopez-Camarillo, Nadia Jacobo-Herrera, Teresa Pusiol, Sandra Rosini, Roberta Zappacosta, Christos Filippos Maramis, Anastasios Delopoulos, Dimitrios Karagiannis, Zehra Safi Oz, Javier Camacho, Ana Ramírez, Chung-Yung Chen, Chin-Hung Wang, Lourdes Gutiérrez-Xicotencatl, Adolfo Pedroza-Saavedra, Lilia Chihu-Amparan, Tanya Plett-Torres, Ana-Maria Gonzalez-Jaimes, Minerva Maldonado-Gama, Fernando Esquivel-Guadarrama, Luis M. Alvarez-Salas, María Luisa Benítez-Hess, Julia Toscano-Garibay, Helen Trottier, Radhakrishna M Pillai, Reshmi G, Davy Vanden Broeck, Gaelle Boulet, Caroline Horvath, Shaira Sahebali, Johannes Bogers

### **© The Editor(s) and the Author(s) 2012**

The moral rights of the and the author(s) have been asserted.

All rights to the book as a whole are reserved by INTECH. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECH's written permission. Enquiries concerning the use of the book should be directed to INTECH rights and permissions department (permissions@intechopen.com).

Violations are liable to prosecution under the governing Copyright Law.

Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2012 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

Legal deposit, Croatia: National and University Library in Zagreb

Additional hard and PDF copies can be obtained from<EMAIL_ADDRESS>
Human Papillomavirus and Related Diseases - From Bench to Bedside - Research Aspects Edited by Davy Vanden Broeck

p. cm. ISBN 978-953-307-855-7 eBook (PDF) ISBN 978-953-51-6725-9


**6.2 Second step: overcoming fears and limitations**

In this second part of the exercise, we ask the patient to write a different list following these instructions:

*All those things that you thought have stopped you from reaching your goals, all those silly things that you said to yourself that made you feel like you might not be able to reach them… maybe all those fears that you had before and that stopped you from success until now.*

In my experience (AT), fear is the principal enemy of setting and achieving your goals, but optimism and good self-esteem will also act as your allies all the way to success.

Once all of these fears and limitations are written on a sheet of paper, we tell the person to say goodbye to them, and tear up the paper with all her/his strength. We tell them:

**125**

*Active-Alert Hypnosis to Achieve Personal, Professional, and Therapeutic Goals*

*small… unrecognizable… insignificant pieces… torn apart…*

*while throwing them away… ¡THAT'S RIGHT!*

wall or an image). Next, we start to suggest:

*we start? Ok, look at that picture1*

**(Arnoldo Téllez, n.d.)**

   Shall*

*point of the picture, whichever you want… Now… please put your hands with your palms facing up resting on your legs… that's right… keep looking at the same point… while you concentrate on your breath… Now… I wonder what sensations are going to emerge within your body… when you start to practice this exercise… we are going to practice an exercise … Now… have you ever felt the sensation of a weight in your hands?… that's right… as if you have some weights in your hands right now… I want you to imagine as if you have some weights in each one of your hands, a pair of dumbbells… one in each hand… about 2 or 3 kilos… is that okay?… Right… Now… You can grab them hard… and feel the weight… 2 or 3 kilos in each hand… Right now… you do not know yet… exactly how you feel… but as you move your dumbbells up and down … they will feel heavier… While you watch that point… start raising one dumbbell … up to your shoulder … Now slowly lower it… While lowing it, raise the other dumbbell… Okay… now move up and down the other one… the left arm and then the other… Right… arm… while still looking at that point… that's right … I want you to continue looking at the same point… Maybe now you can feel how the muscles contract and then relax… I want you to feel the weight of those dumbbells… feel how the biceps, the muscles of the arms get tensed… little by little feeling warm… the energy and calories in your arms are burning… the muscles get tensed and then relaxed … and please keep your eyes open looking at the picture… looking at the same point … and with each movement… each time closer to know how you will be feeling… once you start moving… active… and relaxed… you become more actively relaxed… still focusing on your breathing… now take two or three deep breaths… That's right… now… every time you inhale…    ¡THAT'S RIGHT!*


*Supporting open minds since 2005*

Numerical and Experimental Studies on Combustion Engines and Vehicles http://dx.doi.org/10.5772/intechopen.87852 Edited by Paweł Woś and Mirosław Jakubowski

#### Contributors

Mohamed Ali Mergheni, Semakula Maroa, Freddie Liswaniso Inambao, Enhua Wang, Chenyao Wang, Fujun Zhang, Huasheng Cui, Chuncun Yu, Bolan Liu, Zhenfeng Zhao, Changlu Zhao, S.K. Gugulothu, B. Bhasker, V. V. Phani Babu, Paweł Woś, Krzysztof Balawender, Mirosław Jakubowski, Artur Jaworski, Paulina Szymczuk, Adam Ustrzycki, Simeon Iliev, Gonzalo Moreno Contreras, Daniel Martins, Rodrigo de Souza Vieira, Mohamed Mahdi Belhajbrahim, Toufik Boushaki, Jean-Charles Sautet

#### © The Editor(s) and the Author(s) 2020

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Numerical and Experimental Studies on Combustion Engines and Vehicles Edited by Paweł Woś and Mirosław Jakubowski p. cm. Print ISBN 978-1-83968-032-8 Online ISBN 978-1-83968-033-5 eBook (PDF) ISBN 978-1-83968-034-2


*Article* **Electrochemical Synthesis of Nano-Sized Silicon from KCl–K2SiF6 Melts for Powerful Lithium-Ion Batteries**

**Timofey Gevel 1,2 , Sergey Zhuk 1,2 , Natalia Leonova 1 , Anastasia Leonova 1 , Alexey Trofimov 1,2 , Andrey Suzdaltsev 1,2,\* and Yuriy Zaikov 1,2**

**Abstract:** Currently, silicon and silicon-based composite materials are widely used in microelectronics and solar energy devices. At the same time, silicon in the form of nanoscale fibers and various particles morphology is required for lithium-ion batteries with increased capacity. In this work, we studied the electrolytic production of nanosized silicon from low-fluoride KCl–K2SiF6 and KCl–K2SiF6–SiO2 melts. The effect of SiO2 addition on the morphology and composition of electrolytic silicon deposits was studied under the conditions of potentiostatic electrolysis (cathode overvoltage of 0.1, 0.15, and 0.25 V vs. the potential of a quasi-reference electrode). The obtained silicon deposits were separated from the electrolyte residues, analyzed by scanning electron microscopy and spectral analysis, and then used to fabricate a composite Si/C anode for a lithium-ion battery. The energy characteristics of the manufactured anode half-cells were measured by the galvanostatic cycling method. Cycling revealed better capacity retention and higher coulombic efficiency of the Si/C composite based on silicon synthesized from KCl–K2SiF6–SiO2 melt. After 15 cycles at 200 mA·g−1, material obtained at 0.15 V overvoltage demonstrates capacity of 850 mAh·g−1.

**Keywords:** lithium-ion battery; silicon; halide melt; electrodeposition; silicon fibers; nanotubes; nanoneedles; composite anode

#### **1. Introduction**

Currently, silicon and silicon-based composite materials are widely used in microelectronics, solar energy devices, as well as in the manufacture of portable energy storage devices [1–3]. In particular, a new lithium-ion battery (LIB) with a silicon-based anode is being actively developed. The theoretical capacity of such an anode (4200 mAh·g−1) is an order of magnitude higher than the widely used graphite anode [3,4]. However, during cycling, silicon is subject to huge volumetric expansion (up to 400% [5]), which leads to mechanical destruction of the electrode and significant loss of the original capacity. To avoid this, it is recommended to use anodes based on nanosized silicon particles. Nanosized silicon cannot be obtained by the existing methods of the carbothermal reduction of quartz to silicon with subsequent purification of the silicon by hydrogenation–dehydrogenation [6]. The use of nanosized silicon particles, mainly fibers and tubes within composite materials, can significantly reduce the problem of volumetric expansion. Suitable polymer binders also reduce mechanical degradation of the electrode, but they do not affect the silicon expansion [7,8]. Another method that can improve silicon's performance is the coating (mainly carbon based) of silicon particles and creation of a "core-shell" structure [9,10]. Doped silicon anodes have also shown improved electrochemical performance and cyclic resistance in comparison with pure silicon [11–13].

**Citation:** Gevel, T.; Zhuk, S.; Leonova, N.; Leonova, A.; Trofimov, A.; Suzdaltsev, A.; Zaikov, Y. Electrochemical Synthesis of Nano-Sized Silicon from KCl–K2SiF6 Melts for Powerful Lithium-Ion Batteries. *Appl. Sci.* **2021**, *11*, 10927. https://doi.org/10.3390/ app112210927

Academic Editor: Dong-Won Kim

Received: 25 October 2021 Accepted: 16 November 2021 Published: 19 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Methods for preparing silicon nanofibers can be divided into two groups. The first group comprises methods based on removing excess material-laser ablation [14], ion etching [15], chemical etching [16,17]. These methods allow one to obtain high-purity components with the required sizes. However, they require expensive equipment, catalysts (gold is most often used) and are poorly suited for industrial scale production. The second group of methods includes silicon deposition on the substrates. Among these, the most commonly used method is the vapor–liquid–crystal deposition [18,19]. Silane is usually used as a source of silicon. The disadvantages of these methods also include the use of gold as a catalyst and the difficulty in scaling and hardware design of the process. To obtain silicon suitable for microelectronics, several stages of purification are needed, one of which is represented by a group of methods based on zone crystallization from molten silicon [20].

One of the cheapest and promising methods for producing nanosized silicon is electrolytic reduction from molten salts. To date, many studies have been carried out aimed at the electrolytic production of silicon from various molten electrolytes with K2SiF6, Na2SiF6, SiO2, and SiCl4 additives in a temperature range mainly from 550 ◦C to 750 ◦C [21–39]. All these studies show the fundamental possibility of using electroreduction for the production of silicon deposits of various morphologies, including continuous coatings up to 1 mm thick, submicron films (0.5–1 μm), micro-sized dendrites, nano- and micro-sized disordered fibers.

One of the most promising electrolytes for silicon production is the KCl–KF melt with a KF molar fraction of up to 66% [27–31], which is a good solvent for K2SiF6 and SiO2 and is water soluble. However, this system also has a number of disadvantages, including relatively high aggressiveness of KF to reactor materials, the need to remove impurities such as H2O and HF from KF when preparing a molten KCl–KF mixture, and a good solubility of oxides. Despite the positive results of the work performed, all of the above factors can impede the deposition of high-purity silicon, especially without oxygen inclusions.

To eliminate these drawbacks, alternative media with the reduced or zero fluoride content for the production of silicon are being investigated. In particular, this concerns the KCl–KI–KF–K2SiF6 [32,33] and CaCl2–CaO–SiO2 melts [34–37], although their use also implies the absence of moisture in the reactor and increased requirements for the preparation of molten electrolytes and instrumentation. In previous works, we have shown the fundamental possibility of obtaining nanosized silicon fibers by electrolytic refining of silicon in a low-fluoride KCl–K2SiF6 system [38,39]. The advantage of this system is that its anionic composition virtually does not change. That provides stabilization of the energy characteristics of silicon-containing electroactive ions and possibility of controlling the morphology of silicon deposits. Preliminary electrolysis tests [38,39] show that it is difficult to obtain nanosized silicon deposits from molten salts. In particular, this is due to the varied concentration of silicon-containing ions in the melt, the semiconducting nature of silicon, and increased sensitivity of the deposit morphology to oxygen impurities.

Despite the active interest in Si-based anodes for LIBs [3,4,6–19,40], the works devoted to the utilization of electrolytic silicon deposits as the LIB anode material are limited [41–46]. The highest characteristics for silicon obtained by electrodeposition are on carbon cloth. The capacity was more than 1100 mAh·g−1 by the 200th cycle at a discharge current of 500 mA·g−1, while the silicon nanopowder itself, without the use of carbon fabric, demonstrates very low capacity—less than 500 mAh·g−1 by the 5th cycle and less than 200 mAh·g−1 by 200th cycle [40]. In recent years, most researchers are focused on silicon electrodeposition from CaCl2-based melts. In work [42] carried out by researchers from the USA and China, electrodeposited nanofibers in LIB demonstrate a capacity of 714 mAh·g−1 after 500 cycles at C/2 current. According to [45], China launched a largescale test production of electrodeposited silicon with capacity more than 2000 mAh·g−1 after 70 cycles at a current of 0.1 C; capacity at a current of 5 C is about 1600 mAh·g−1. It is

noted that for practical use of the produced silicon they made Si/graphite mixture with a total capacity of 650 mAh·g−1 [47].

In this regard, in this work, we studied the effect of SiO2 on the morphology of nanosized silicon deposits during the electrolysis of KCl–K2SiF6 melts. For this, silicon deposits were obtained from the KCl–K2SiF6 and KCl–K2SiF6–SiO2 melts at different cathode overvoltage. Obtained deposits were analyzed and tested on their electrochemical characteristics as a part of an Si-based anode of a LIB.

#### **2. Materials and Methods**

*Melt preparation.* The investigated KCl electrolytes with additions of K2SiF6 and SiO2 were prepared by mixing individual salts of a chemically pure grade (Reakhim, Russia) and then melting them in a glassy carbon crucible immediately before the experiments. In a previous study [48], it was shown that the purity of KCl used is close to that of the recrystallized salt; therefore, additional purification of the prepared melt from electropositive impurities was not carried out. The K2SiF6 salt was preliminarily purified from oxygen impurities by HF fluorination. For this, K2SiF6 was mixed with NH4F and heated in stages to a temperature of 450 ◦C according to a previously described procedure [49]. SiO2 was added to the melt in an amount of 0.34 wt.%. The KCl–K2SiF6 and KCl–K2SiF6–SiO2 melts were held for an hour at the operating temperature to establish equilibrium between silicon ions [50,51]. Then, the melt samples were taken for analysis, and electrolysis was started.

*Experimental setup.* Electrolysis tests were carried out in a sealed quartz retort with a high-purity argon atmosphere at a temperature of 790 ◦C (Figure 1). A glassy carbon crucible with the melt was placed at the bottom of the retort, which was hermetically closed with a fluoroplastic cap. The inner walls of the retort were additionally shielded with nickel plates to protect against fluorine-containing sublimates. Holes with fittings were made in the cap, in which a working electrode (glassy carbon), a glassy carbon counter electrode, and a silicon QRE were placed. In addition, holes were provided for an additional working electrode, thermocouple, and a tube for gas supply and loading of silicon-containing additives. During the experiments, the temperature was controlled within ±2 ◦C by a Pt/PtRh thermocouple and a thermocouple module USB-TC01 (National Instruments, Austin, TX, USA).

**Figure 1.** Experimental setup. 1—working electrode; 2—gas inlet; 3—quartz cell; 4 —counter electrode; 5—quasi-reference electrodes; 6—glassy carbon crucible.

*Electrochemical measurements and electrodeposition.* To establish the effect of SiO2 on the kinetics of silicon electrodeposition, voltammograms were obtained by the cyclic chronovoltammetry. Before measurement, the electrodes were kept in the melt for 30 min to establish a stable (within ± 5 mV) potential difference between the working electrode and the quasi-reference electrode (QRE). In order to determine and compensate the ohmic voltage drop in the measuring circuit, impedance spectroscopy and current interruption (I-Interrupt) technique were used.

The electrodeposition of silicon was carried out on glassy carbon plates with an area of 2 cm2 at different cathode overvoltage for one hour. The electrolysis time was selected based on the results of preliminary electrolysis tests [24,25]. For electrochemical measurements and electrolysis, PGSTAT AutoLAB 302N with Nova 1.11 software (MetrOhm, Herisau, Switzerland) was used. Glassy carbon plates were used as anodes.

At the end of the electrolysis, the electrode was raised above the melt for 20–30 min in order to remove excess salts included in the cathode deposit. Then, the electrode was raised to the cold zone of the retort and, after cooling in an inert atmosphere, was removed.

*Separation of the cathode deposit.* The obtained silicon deposits were washed in acidic (HCl) water solution; the pH of the solution for washing varies in the range of 2 to 4. An ultrasonic homogenizer SONOPULS UW mini 20 was used to disperse the deposits. Dispersion was carried out in a periodic mode at a given power of 0.995 kJ with a pulse duration of 90 s. After washing, the deposits were dried in a vacuum oven at 80 ◦C for 18 h.

*Analysis of the morphology and composition.* The elemental composition of the melt and the obtained silicon deposits was analyzed by atomic emission spectroscopy with inductively coupled plasma (AES-ICP) using an iCAP 6300 Duo Spectrometer (Thermo Scientific, Waltham, MA, USA). The morphology and elemental composition of the obtained deposits were studied using a Tescan Vega 4 (Tescan, Kohoutovice, Czech Republic) scanning electron microscope with an Xplore 30 EDS detector (Oxford, UK).

*Electrochemical performance.* Silicon performance was investigated in a 3-electrode half-cell. The anode composition was 10 wt.% polyvinylidene difluoride dissolved in N-methyl-2-pyrollidone, 10 wt.% carbon black, and 80 wt.% silicon. LIB fabrication was performed in an argon-filled glove box (O2, H2O < 0.1 ppm). Stainless steel mesh with applied composite anode was used as the working electrode and two separate lithium strips as the counter and reference electrodes. All electrodes were divided by two layers of separator and tightly placed in the cell. The cell was flooded with 1 mL of electrolyte—1 M LiPF6 in a mixture of ethylene carbonate/dimethyl carbonate/diethyl carbonate (1:1:1 by volume). Cycling experiments were performed using a Zive-SP2 potentiostat (WonATech, Seoul, Korea).

#### **3. Results**

#### *3.1. Electrolysis Test Results*

According to ICP, the silicon contents in the KCl–K2SiF6 and KCl–K2SiF6–SiO2 melts before electrolysis tests were 0.05 and 0.15 wt.%, respectively. The deposits were obtained at cathode overvoltage of 0.1, 0.15, and 0.25 V relative to the QRE potential. For the KCl– K2SiF6 melt (0.05 wt.% of silicon), the obtained deposits are shown in Figure 2. It can be seen that the growth of deposits occurs with the formation of silicon-salt mixtures. In this case, with a decrease in the cathode overvoltage, the deposits change color from gray to light brown. This can be associated both with a change in the crystalline structure of silicon to amorphous, and with a change in the size of silicon particles [31,38].

**Figure 2.** Photographs of the cathode deposits obtained after the electrolysis of the KCl–K2SiF6 melt at a temperature of 790 ◦C and cathode overvoltage of 0.25, 0.15, and 0.1 V vs. QRE potential.

Electrolysis tests in the KCl–K2SiF6 melt with the addition of SiO2 (0.15 wt.% of silicon) at the same cathode overvoltages of 0.1, 0.15, and 0.25 V vs. QRE potential were performed. As a result, cathodic deposits were obtained, photographs of which are shown in Figure 3. It can be noted that during electrolysis of the melt with the addition of SiO2, more volumetrically uniform deposits are obtained, while the color of the precipitates remains the same in all tests.

**Figure 3.** Photographs of the cathode deposits obtained after the electrolysis of the KCl–K2SiF6–SiO2 melt at a temperature of 790 ◦C and cathode overvoltage of 0.25, 0.15, and 0.1 V vs. QRE potential.

#### *3.2. Effect of SiO2 on the Morphology of Silicon Deposits*

For more detailed analysis of the effect of SiO2 on the morphology of the silicon, SEM images were made. Figure 4 shows the corresponding micrographs of the deposits. It can be seen that in the KCl–K2SiF6 system, silicon was deposited in the form of disordered fibers with an average diameter of 300–400 nm, while the effect of cathode overvoltage on the morphology of the deposit is not clearly observed. Such a result is probably due to the limiting stage of the preceding chemical reaction of silicon-containing ion dissociation.

**Figure 4.** Micrographs of the cathode deposits obtained during the electrolysis of KCl–K2SiF6 and KCl–K2SiF6–SiO2 melts at a temperature of 790 ◦C and cathode overvoltage of 0.25, 0.15, and 0.1 V vs. QRE.

When SiO2 is added in the melt, silicon is predominantly deposited in the form of nano-sized tubes and needles, the length of which varies in the range of 30–60 μm, and the diameter varies in the range of 200 nm to 400 nm. This effect can be interpreted as the mutual influence of several factors:

(3) a change in the composition of silicon-containing electroactive ions, as a result the ions activity and the rate constant of the possible preceding chemical reaction in the melt change.

Taking into account the weak effect of the cathode overvoltage on the morphology of silicon deposits, the effect of SiO2 addition can be associated with the last two factors. It can be noted that the average size of silicon particles decreases with the addition of SiO2 to the melt. A similar effect was observed in other works [31,41].

According to EDX analysis, the average oxygen content in the fibers obtained during the electrolysis of the KCl–K2SiF6 melt was 2.1–4.3 wt.%, and the average oxygen content in nano-sized tubes and needles obtained during the electrolysis of this melt with the addition of SiO2 was 2.7–5.1 wt.%. The appearance of oxygen in the deposits can be caused both by the use of a quartz retort as a reactor container and after-electrolysis silicon treatment. It is important to note that the addition of SiO2 does not significantly increase oxygen content in silicon deposits, which is important from the point of the electrochemical characteristics as the anode of LIB.

#### *3.3. Effect of SiO2 on the Kinetics of Silicon Electrodeposition*

To check the effect of SiO2 addition on the silicon electroreduction, voltammograms were recorded in the KCl–K2SiF6 and KCl–K2SiF6–SiO2 melts at a temperature of 790 ◦C. Figure 5 shows that the electroreduction of silicon-containing electroactive ions from the KCl–K2SiF6 melt occurs at a potential more negative than 0.1 V, with the formation of a cathodic peak **Si** [27,32] at a potential of –0.1 V, relative to the QRE potential (Figure 5, black line).

**Figure 5.** Cyclic voltammograms for the KCl–K2SiF6 and KCl–K2SiF6–SiO2 melts with a total silicon content of 0.05 and 0.15 wt.%, respectively. Potential sweep rate is 0.4 V s−1.

For the SiO2-containing melt, additional **Si-O** cathode waves appeared on the voltammogram at the potentials of about 0.1 and 0 V relative to the QRE potential (Figure 5, red line). Those waves can be associated with the discharge of different oxygen-containing electroactive Si-O-Cl-F anions, which appear in the melt during SiO2 dissolution [50,51]. A similar effect was observed in the works, aimed at the electroreduction of silicon and other elements from halide-oxide melts [52–54]. In the KCl–K2SiF6–SiO2 melt, an increase of the silicon electroreduction current was observed, since the silicon content has been

increased. In the anode region, peak **Si** and a wave **(Si-O)** at a potential of 0.1 and 0.33 V relative to the QRE potential were detected. We assume that the peak **Si** is associated with silicon dissolution [27,32], and the additional wave **(Si-O)** can be caused by oxidation of a reduced form of the silicon electroactive ion. To obtain a more detailed picture, further study of the cathodic process in KCl–K2SiF6 and KCl–K2SiF6–SiO2 melts is required.

#### *3.4. Electrochemical Characteristics of the Obtained Silicon Deposits*

The obtained silicon deposits were studied as a composite Si/C anode for a lithiumion battery. In the first charge/discharge cycle (Figure 6), the capacity of Si/C composite prepared on the base of silicon electrodeposited from KCl–K2SiF6 was between 1450 and 3000 mAh·g−1 during lithiation, and between 950 and 1670 mAh·g−1 during delithiation. Despite of the similar morphology and size of silicon fibers synthesized at different overpotentials, their energy characteristics are different. As can be seen from Figure 6, samples 2 and 3 synthesized at 0.15 and 0.25 V show similar charging times, although the initial period associated with electrolyte reduction and solid electrolyte interphase formation is quite different. Silicon synthesized at 0.1 V demonstrates 60% lower capacity, and the highest initial coulombic efficiency of 65.5%.

**Figure 6.** First charge/discharge cycle curves of Si/C composites based on electrodeposited silicon at 50 mA g−1. Solid lines represent silicon deposited from molten KCl–K2SiF6; dashed lines represent silicon deposited from molten KCl–K2SiF6–SiO2.

After 15 cycles at 200 mA·g−1 (Figure 7a), the capacitance for the best sample of silicon fibers was 750 mAh·g−1 for sample 3 synthesized at 0.25 V overvoltage. The coulombic efficiency of the anode half-cell with silicon fibers in this case increased from 59% up to 92%. Silicon synthesized at 0.15 V (sample 2) demonstrates a similar capacity fade rate as sample 3, but a lower overall capacity. However, these samples have different coulombic efficiency during the first 10 cycles. Sample 1, while having a much lower capacity, demonstrates better capacity retention, with −50.7% of the initial capacity after 15 cycles.

**Figure 7.** Cycling performance and the coulombic efficiency of the Si/C made from silicon electrodeposited from (**a**) KCl–K2SiF6 (samples 1–3) and (**b**) KCl–K2SiF6–SiO2 (samples 4–6) at the cathode overvoltages of –0.1, –0.15, and –0.25 V. Diamonds-coulombic efficiency; squares-capacity.

For silicon samples obtained from the KCl–K2SiF6–SiO2 melt, the anode capacity was between 920 and 2430 mAh·g−1 for charging and between 600 and 1300 mAh·g−1 during discharge. As can be seen from Figure 6, the behavior of these samples is similar to the ones synthesized without the addition of SiO2 to the melt, but lower capacities are achieved for all the samples. The highest capacity value was observed for the sample 5 synthesized at 0.15 V overvoltage. The initial coulombic efficiency of the silicon needles/tubes was 44–65%, lower than for the samples 1–3. Sample 4 demonstrates much higher delithiation potential than all other samples, probably due to the higher C-rate in comparison to other samples. After 15 cycles (Figure 7b), samples synthesized from the melt with SiO2 addition demonstrate higher coulombic efficiency—between 93.5% and 96.1%—and better capacity retention rate. Remaining capacity of the sample 5 is 850 mAh·g−1, which is 74.5% of the initial capacity.

It is probable that such a difference in capacity fade rate is caused by different silicon structures or surface layer composition; however, the reasons for drastic capacity difference among the samples with similar morphology synthesized at different deposition overpotential is not clear. The results obtained indicate that further research is promising, aimed both at optimizing the conditions for obtaining silicon deposits from the investigated molten electrolytes, and at improving the performance of a lithium-ion battery with a composite anode based on the obtained silicon.

#### **4. Conclusions**

In this work, we studied the effect of cathode overvoltage and the addition of SiO2 on the morphology of silicon electrolytic deposits obtained from KCl–K2SiF6 and KCl–K2SiF6– SiO2 melts at a temperature of 790 ◦C. Using scanning electron microscopy, it was shown that during electrolysis of the KCl–K2SiF6 melt, nano-sized disordered fibers (300–400 nm in diameter) are formed on the cathode, while the addition of SiO2 to the melt leads to an ordered growth of silicon deposit in the form of nano-sized needles and tubes (diameter 200–400 nm, length 30–60 μm). At the same time, it was noted that an increase in cathode overvoltage during the electrolysis of the studied molten electrolytes has virtually no effect on the morphology and size of the deposit, which may be associated with the course of the process under the conditions of a slowed-down preceding chemical reaction of the silicon-containing ion dissociation.

According to EDX analysis, the average oxygen content in silicon deposits obtained during the electrolysis of the KCl–K2SiF6 melt was 2.1–4.3 wt.%, and in the deposits obtained during the electrolysis of this melt with the addition of SiO2, the average oxygen content was 2.7–5.1 wt.%.

The resulting deposits were used for the manufacture of composite Si/C anode halfcells of lithium-ion batteries. All the synthesized samples have similar initial charge/discharge curves, although the capacity is different. Samples synthesized at 0.1 V show much lower capacity than the ones synthesized at 0.15 and 0.25 V overvoltage. Although the first cycle capacity of silicon synthesized with SiO2 addition is lower than that of silicon obtained without additive, further cycling results in higher capacity and much higher capacity retention for all samples. For silicon deposited from KCl–K2SiF6, the highest capacity value is 750 mAh·g−1 with capacity retention of 46.6 % and coulombic efficiency of 92.7%. The best results are achieved for silicon synthesized from KCl–K2SiF6–SiO2 at 0.15 V overvoltage; its capacity was 850 mAh·g−1 after 15 cycles at a current of 200 mA·g−1 and capacity retention of 74.5% with coulombic efficiency of 96.1%. Such differences in the capacity retention for the samples synthesized with and without SiO2 addition is not clear and requires further investigation.

**Author Contributions:** Conceptualization, T.G. and A.S.; methodology; T.G., S.Z., N.L., A.L. and A.T.; validation, T.G. and A.S.; formal analysis, A.S. and A.T.; investigation, T.G., S.Z., N.L., A.L. and A.T.; writing—original draft preparation, T.G. and A.S.; writing—review and editing, T.G. and A.S.; supervision, A.S. and Y.Z.; project administration, A.T. and A.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** This work is performed in the frame of the State Assignment number 075-03- 2020-582/1, dated 18 February 2020 (the theme number 0836-2020-0037).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


*4.4. Cell Culture and MTT Assay*

Human liver cancer cell lines (HepG2) and a normal fibroblast NIH-3T3 cell line (both cell lines procured from NCCS, Pune, India) were seeded in 96-well plates with a cell population of 1 × 10 4 cells/well in DMEM/F12 with antibiotic solution and 10% FCS (Invitrogen, CA, USA). Cells were incubated in a 5% CO2 chamber at 37 ◦C. The monolayer cultured cells were washed with PBS, and then were treated with EO (10–100 μg/mL) with various test concentrations of tested samples in serum-free media and incubated for 24 h. The medium was aspirated, 0.5 mg/mL of MTT reagent was added, and the solution was incubated at 37 ◦C for 4 h. After the incubation period, measurements were carried out according to the method protocol described by Khalil et al. (2021) [5].


**3. Age of onset**

326 Type 1 Diabetes – Complications, Pathogenesis, and Alternative Treatments

21,0/100000/year from 1977 to 1983 for children aged less than 19 year old. Another study for the same population found that the incidence of T1DM had increased from 22,7/100000 in 1984 to 26,0/100000 in 1993 and this increase of about 2% a year, though small, is statistically significant and the effect over 10 years is a large increase [16]. An important increase in incidence of T1DM was also observed in Saxony between the five year periods 1999-2003 and

Ιn our study we have ascertained the mean annual incidence on T1DM in the Greek Cypriot population during the period 1990 - 1999 in children younger than 15 years of age. During this period the incidence of T1DM was 10.76 /100000[18]. In order to identify an increase in the incidence of T1DM in our country, as occurred in the majority of the populations worldwide, we had performed an analysis of the newly diagnosed cases until the end of the year 2004. There was a statistically significant increase in the incidence during the period 2000-2004 with an estimated mean overall incidence 11.9/100000[19]. We had subsequently extended this work by adding the new cases of the five year period 2005 – 2009 in order to document this rising trend by comparing the incidence between the two decades (1990-1999 vs 2000-2009). We have observed a rising trend of the mean incidence from 10.76/100.000 at the first decade (1990-1999) of the study up to 14.4/100.000 at the second decade (2000-2009). According to Wilcoxon two-sample test this increasing trend of incidence during the 20 years analysed is statistically significant (p-value=0.0091). The mean incidence rate for each 5 year- period in accordance with the population data (population below the age of 15yr) is presented in figure 2. The overall mean incidence of T1DM, in the Greek Cypriot population was 12.46/100000 during the twenty-year period 1990-2009. This raised

2004-2008 with estimated rates 15.7/100000 and 19.2/100000 respectively [17].

incidence classifies Cyprus among the countries with high incidence of T1DM.

Fig. 2. Different annual incidence rates during five year periods.

T1DM formerly called as juvenile diabetes it is one of the most common chronic disease of youth as 80% of individuals with T1DM are younger than 20 year of age [20-21]. The age of manifestation of childhood onset T1DM has a bimodal allotment with one peak at 4 to 6 years of age and a second in early puberty (10 to 14 years of age) [22-23]. Recent studies report a higher rate of increase among children younger than 5 years than in children between 5 and 15 years of age [24-25]. This may be related to an earlier onset of clinical manifestation or to a true increase in the causative factors of the disease.

Although the clinical appearance occurs at all ages [21] one fourth of individuals with T1DM are diagnosed as adults [26]. Up to 10% of adults primarily supposed to have type 2 diabetes are found to have antibodies associated with T1D [27] and beta cell destruction in adults seems to take place at a much slower rate than in young T1D cases, often delaying the need for insulin therapy after diagnosis.


An increasing volume of data suggests the putative biological importance of decavanadate, a vanadate oligomer that eventually occurs in the cytoplasm more often than expected (51). Specific interactions of decavanadate have been clearly demonstrated for Ca2+-ATPase, myosin, and actin, considered as major proteins in muscle contraction and its regulation. Based on crystal structure data, the binding of the SERCA inhibitory decavanadate was localized (19, 52) to the ATP binding site between the cytoplasmic domains A, N, and P of the thapsigargin-inhibited enzyme in the absence of Ca2+ as shown by **Figure 1B** of the present paper.

Vanadate compounds show a significant antidiabetic efficacy. Sodium vanadate was applied in diabetes therapy 22 years before the first use of insulin to treat diabetes in human beings (53, 54). Besides its insulin-mimetic action, vanadate inhibits the glucose-6-phosphatase (G6P) enzyme, with a key role in glucose metabolism.

The proposal of Kramer et al. (55) to consider vanadium diascorbate with a molar mass of 403 Da as a candidate EDLF is worth mentioning. However, this hypothesis has not been confirmed since vanadate ions are not ubiquitously distributed in mammals and are toxic in particular by accumulation in some organs.

#### **ATPase INHIBITORY MCS-FACTORS**

Our way to disclose the structure of the assumed EDLFs is a typical example of serendipity. At the end of 1990s, we investigated at the Max-Planck Institute for Biochemistry in Munich an herbal product isolated from the roots of *Helleborus species.* The plant product contained, besides other components, the cardiac glycoside hellebrin with strong NKA-inhibitory potency. The chemical stability of hellebrin was monitored by measuring the inhibition of Na+/K+-ATPase, starting therewith a very intensive and prolific collaboration with Hans-Jürgen Apell and Robert Stimac from the University of Konstanz (GER), which led finally to the disclosure of the novel ATPase-inhibitory factor.

The alkaline treatment destroyed hellebrin and annihilated the NKA-inhibitory effect but, as the alkaline boiling was accidentally prolonged for several hours, a very potent novel NKA inhibitor was generated (56). By the HPLC on RP-18 column, the pure inhibitory compound eluted closely after the injection peak, or delayed if it was attached to some lipophilic components. Similar characteristics have been reported by the HPLC analysis of the earlier EDLF preparations from biological samples (38–43).

The main component of the plant material subjected to alkaline boiling was a resin-like compound, similar to the polymeric carbon suboxide (57). Therefore, it was thought that the obtained potent ATPase inhibitor could be a low molecular weight decomposition product of this polymer. For the structure of the de-polymerization product, we assumed a repeatedly condensed 4-pyrone frame that forms a supplementary cage-type macrocycle with formula (C**3**O**2**)**n** where (n = 4, 6, or 8). We named the inhibitor MCS, macrocyclic carbon suboxide (56). Tentatively, this MCS was suggested as probable EDLF and natriuretic factor (58).

MCS factors showed a rigorously reproducible potent inhibition of the Na+/K+-ATPase, Ca2+-ATPase, H+/K+-ATPase, and K-dp-ATPase with IC50 values in the 0.2–0.5µg/mL range. The mechanism of Na+/K+-ATPase inhibition by the MCS factor was investigated with the fluorescent styryl dye RH421, a dye known to reflect changes of local electric fields in the membrane dielectric. It was found that the binding of the MCS to the Na+/K+-ATPase is not competitive with ouabain (59, 60). MCS factors interact with the Na+/K+-ATPase in the E1 conformation of the ion pump and induce a structural rearrangement that causes a change of the equilibrium dissociation constant for one of the first 2 intracellular cation binding sites. The MCS-inhibited state was found to have bound one cation (H+, Na+, or K+) in one of two non Na+ specific binding sites, and the other Na+ ion was bound at high Na+ concentrations to the highly Na+-selective ion-binding site (60).

The proposal with cage-form condensed macrocyclic carbon suboxide structure was apparently supported by the mass ion peaks (m/z) containing multiples of the 68.03 Da unit, the molar mass of the C**3**O**2**.

The main m/z peaks in ESI-MS spectra (**Figure 2**) have been assessed as small multiples of the carbon suboxide unit (68 Da) with 1 Na+ ion according to the formula [(C3O2)**n**·Na]+ thus, m/z = 159 Da correspond to *n* = 2; 295 Da (*n* = 4); 431 Da (*n* = 6), and 567 Da (*n* = 8). The small MH+ peaks at 275, 409.2, and 544.2 Da were also perceptible in the mass spectrum (56).

Interestingly, a molar mass ion at M = 408 Da (MH+ = 409; MNa+ = 431 Da) was identified in some earlier EDLF preparations from biological sources, e.g., human plasma (61), placenta (62), or bound to a hypertension-associated plasma protein (63). It can be speculated about the possible identity of these factors and our MCS product but only the same molar mass ion value is not sufficient to prove or disprove this identity. The fine structure of the mass spectra may also differ due to the different ionization techniques, i.e., FAB used by Weiler et al. (63) and ESI-MS applied by us (56).

Although the ATPase-inhibitory effect of the MCS factors on several P-type ATPases and the mechanism of action on NKA were rigorously reproducible, the structure with head-to-tail condensed pyran-4-one rings supplementary bond in a cage-like macro-cycle could not be confirmed by synthesis despite huge experimental efforts with Frank Freudenmann and Luis Moroder at the MPI for biochemistry in Munich.

Likewise not confirmed were the specific **13**C-NMR signals and the UV-absorbance peaks expected for the macrocyclic condensed pyran-4-one structure. The assessment of the mass spectrum as multiples of a 68 Da unit was correct but the attribution of this mass to C**3**O**2** was erroneous. These disagreements required the revision of the proposed macrocyclic cage-structure.

#### **SPHERICAL OLIGO-SILICIC ACID (SOSA)**

The decisive hint to disclose the actual chemical structure of our NKA-inhibitory factor came from revision of the blind probe of the described alkaline preparation. Surprisingly, the several hours boiling of the NaOH solution alone, without any other reagent yielded a similarly potent ATPase inhibitor as that obtained by alkaline boiling of the plant polymer.

The mystery was explained by identifying small amounts of sodium silicate in the solution leached from the glass flask by its prolonged alkaline heating in oil bath at 120°C. Applying the proper neutralization-activation procedure (56), this silicate was transformed to the highly active ATPase-inhibitory factor identified as spherical oligomers of silicic acid. The generation of biologically active oligomeric condensation products from the inactive monosilicic acid was totally unexpected and thus very surprising.

We considered that this finding could have implications in clearing of some controversial disputes within the following research areas:

Regarding the biological role of silicon, it was generally agreed that Si provides structural support in plants and is beneficial of bones and elasticity of cartilages in animals. But neither a Si containing biologically active substance, nor a protein, which needs Si has been found in animals. Identifying the SOSA as biologically active water-soluble Si compound (64), a decisive argument has been provided in support of the assumed biological role (essentiality) of this element.

The assumed existence of EDLFs and their putative identity with cardiac glycosides was the subject of debates for several decades. The proposed identity of EDLF (65) with the SOSA was a novel approach suggesting the reconsideration of the divergent opinions.

The structure of the ATPase-inhibitory SOSA is formed by successive condensation of a few (oligos in Greek) molecules of monosilicic acid H**4**SiO**4** according to the equation (Eq. 1) where "*n*" should be in the range of 16–200.

$$\begin{array}{rcl} \text{nH}\_4\text{SiO}\_4 & \rightarrow & \begin{bmatrix} \text{SiO}\_x(\text{OH})\_{4-2\text{x}} \end{bmatrix}\_{\text{n}}\\ \text{mono-sillicic acid} & \text{Olgo-sillic acid} \\ \text{x} = 1.5 & \rightarrow & \begin{bmatrix} \text{SiO}\_{1.5} \text{ (OH)} \end{bmatrix}\_{\text{n}} \end{array} \tag{1}$$

The value *x* = 1.5 in the general formula [SiO**x**(OH)**4-2x**]**n** is congruent with a particular symmetry of the multi-cyclic silicic acid oligomers corresponding to polyhedral symmetry, i.e, prismatic hexamer (*n* = 6), cubic octamer (*n* = 8), and prismatic decamer (*n* = 10) structure, known as silsesquioxanes with general formula [SiO**1.5**OH]**n** and shown by **Figure 3**.

The spherical form of the oligo-silicic acid SOSA is accomplished for *x* = 1.5 in the general formula [SiO**x**(OH)**4-2x**]**n** and

is assumed as the natural continuation of the polyhedral series with formula [SiO**1.5**(OH)]**n** for values of *n* > 20. There is an interesting formal resemblance with the series of Platonic bodies (tetrahedron, cube,. . .sphere) from geometry.

Actually, the cage-type condensed polyhedral silsesquioxanes have all Si-OH groups at the external vertices with nearly axial orientation. A similarly external distribution of the Si-OH groups is accomplished by the here disclosed nearly spherical oligomers of the silicic acid. SOSA molecules as next term in the series of polyhedral structures with the same general formula [SiO1.5(OH)]n have the same ratio between the Si: O: H atom = 1: 2.5: 1.

But, there are significant differences between the chemical structure and properties of the polyhedral and of the spherical silica. The predominant difference is that polyhedral silsesquioxanes inhibit neither Na+/K+-ATPase nor other ATPases conversely to the strong ATPase-inhibitory SOSA. Structurally, all polyhedral silsesquioxanes (**Figure 3**) comprise only Q3 type Si atoms, i.e., each one is involved in three (Si)-O-Si bonds and one (Si)-OH bond.

The spherical shape of the oligo-condensed silicic acid with *x* = 1.5 is accomplished by some preferred "*n*" values. **Figure 4** shows the SOSA molecule with *n* = 36, as ball and stick model. This SOSA molecule with formula [Si**36**O**54** (OH)**36**] comprises in its internal shells 4 + 8 = 12 Si atoms of type Q**4** (without Si-OH bonds). In the external shell, there are 12 Si atoms of type Q3 (with one Si-OH bond) and 12 Si atoms of type Q**2** (with two Si-OH bonds). It is observed that the 36 external Si-OH bonds are displayed on the external surface strongly facilitating the hydrophilic interactions with proteins.

Applying the gel-permeation chromatography (GPC) method, molar mass values in the range of 1.2–6.0 kDa have been obtained of the spherical condensed silicic acid oligomers. For the SOSA molecule illustrated on **Figure 4** there resulted *M* = 3.2 kDa, corresponding to molecular diameter φ = 2.2 nm further confirmed by dynamic light scattering (DLS).

The mass ion peaks of SOSA obtained by ESI-MS technique were practically the same as those identified in the spectrum of our MCS factor (**Figure 2**). Actually these m/z peaks correspond to small ionic fragments resulted by the split of the large SOSA molecules in the ionization chamber. The main mass ion peaks

correspond to the formula [(Si**2**O**5**)**q**·Na]+ with m/z = 159 Da for *q* = 1; 295 Da for *q* = 2; 431 Da for *q* = 3, and 567 Da for *q* = 4. Similar mass spectra have been observed by decomposition of condensed silsesquioxanes (66, 67).

In fact, the erroneous assessment of the mass ion peaks as derived from carbon suboxide was caused by the accidental identity of the molar mass of C3O2 M = 68.03 Da and the mass of the 1/2 Si**2**O**5** (di-silicate) ion with M = 68.08 Da, resulted by the decomposition of SOSA. In conclusion, the mass ion peaks on **Figure 2** correspond rather to the formula [(Si**2**O**5**)**q**·Na]+ of SOSA and not to the formula [(C3O2)**n**·Na]+ as initially suggested.

#### **SOSA AS POSSIBLE ENDOGENOUS FACTOR**

With the confirmed potent inhibition of several P-type ATPases, SOSA fulfills the main condition as a candidate ATPase ligand. Its classification as an endogenous factor requires further

that SOSA should be produced within the organism, tissue, or cell. This condition is satisfied with the presumed biosynthetic pathway of SOSA by spherical oligomerization of the monosilicic acid (H4SiO4) ubiquitously distributed in plants and animals (68, 69).

A human body contains approximately 1 g of Si in various combinations with oxygen (generic name: silica). Almost all silica in the body is bound to biomolecules and tissues and only a minor part circulates as dissolved silicic acid in blood plasma and urine (70). The plasma level of silicic acid is 0.7 mg/L corresponding to 0.2 mg/L silicon. Infants have two- to three-fold higher Si plasma level, but in contrast aged persons and pregnant women have significantly lower values of Si. For human beings, the daily ingested amount of Si is estimated to be 30–50 mg and the same amount is eliminated in urine. After meals, the plasma concentration of Si increases 30–50%, and returns after a few hours to the initial level (71). The regulatory mechanisms of Silicon homeostasis require elucidation.

The catalytic action of a biomolecule to accomplish the spherical oligomerization of silicic acid yielding SOSA is presumed. Proteins catalyzing high-grade polymerization of silicic acid have been found (67) in algae (silaffin) or sponges (silicatein). Biomolecules favoring the spherical oligomerization of silicic acid have not yet been identified.

The mechanism of ATPase regulation by the probable endogenous ligand SOSA is not fully understood. According to the unusual physical–chemical properties of SOSA and to its specific interactions with the Na+, K+, H+, Mg2+, or Ca2+ ions and with the ATPase protein, some challenging proposals for the regulation mechanism may be formulated.

#### **ASSUMED REGULATORY MECHANISM OF SOSA**

Transmembrane ion pumping may be physically influenced by SOSA located in a receptor cavity along the ion-transport pathway of the ATPase molecule. The SOSA molecule should behave like a multi-anionic gel with selective binding or permeability effects on cationic species depending on their concentration, charge and size.

The interaction of SOSA with the ATPase protein is assumed on the cytoplasmic site of the ATPases in the E1 conformation as revealed by charge transfer investigations with the dye RH421 (60). According to these data, SOSA should inhibit the X1E1 state, manifesting complex interactions with different X ions (Na+, K+, or H+) and with Na+ ions in the (Na)NaE1 state. Structural details of the very complex binding of the sodium ions to the NKA in the state preceding phosphorylation have been disclosed by high-resolution crystal structure (72).

The ion-sensitive nature of our ATPase inhibitory factors MCS (disclosed as SOSA) was remarked in Ref. (60) assuming that the concentration of the Na+ and of other ions may cause significant structural rearrangements. The *in vitro* ion-sensitive structure of SOSA is also supported by DLS measurements. Tentatively, it could be assumed that the SOSA structure at [Na+] < 5mM inhibits the sodium pump but the structure at [Na+] ≥ 5 mM should activate it. A possible activation mechanism could be by favoring the differentiate binding of Na+ ions to the E1 conformation and promoting the phosphorylation step. Accepting this hypothesis, the generally found cytosolic level of [Na+] = 5.0 mM in the eukaryotic cells could be a consequence of the structural change of the archaic ligand SOSA, which happens accidentally at this Na+ ion concentration.

The here proposed probably archaic regulatory mechanism of the NKA pump by ion-dependent structural changes of the endogenous ligand could also work for other ATPases. The ion concentration threshold for the activation of other ATPases should depend upon the concentration of the ions to be pumped and their interaction with SOSA. This regulatory mechanism, assuming the ion-sensitive structural variation of the archaic ligand SOSA, can explain the astonishing manifoldness of the same well-conserved ion-pumping mechanism in the hitherto identified more than 200 different P-type ATPase pumps.

#### **CONCLUSIONS AND OUTLOOK**

With almost all Si-OH bonds disposed on the external surface of SOSA, this substance should bind preferentially to hydrophilic domains of the target proteins. In P-type ATPases, the wellconserved phosphorylation site between the cytoplasmic domains P and N provides an adequate binding site for SOSA molecules similar to decavanadate (**Figure 1**).

Although SOSA inhibits NKA at sub-micro-molar concentration, its direct competition with ouabain for the hydrophobic steroid binding site in NKA is less probable. The non-competitive binding mechanism of our hydrophilic ATPase inhibitor and ouabain was confirmed by the fluorescence dye measurements of Stimac et al. (59, 60). An apparent competition may appear if the NKA conformation required for SOSA binding and that required for ouabain binding are different (C. Toyoshima, personal communication).

The ion-concentration dependent structural changes of SOSA suggest a probable archaic regulation mechanism of the sodium pump and of other ATPases where the pump ligand is sensing the nature and molarity of the ions to be pumped. It is a challenging idea that the transmembrane ion pumping with fundamental importance for many essential life processes should be regulated by the cation-sensitive structure of an inorganic acid.

The identified chemical properties and enzymatic activities of SOSA are congruent with the predicted characteristics of the EDLFs of the P-type ATPases. **Table 1** shows a synoptic presentation of the assumed characteristics of the putative EDLF factors in comparison with that of candidate substances: ouabain, marinobufagenin, vanadate, and the here disclosed SOSA.

*Summarizing* the characteristics of the SOSA, it may be concluded that these match the predicted criteria of the endogenous ligands of the NKA and probably of further P-type ATPases:

ND: not determined.

It is planned to obtain further structural details of the interaction of SOSA with ATPase proteins with possible implication for the regulation of the pump. One of the very intriguing questions is to investigate the influence of the concentration of Na+, K+, Mg2+,Ca2+, and other ions on the structure of SOSA. The further great challenge for ongoing research is to establish the role of SOSA in ATPase related cellular and physiological processes and to explore its possible health-care applications in some connected pathologies (73, 74).

#### **ACKNOWLEDGMENTS**

We are grateful to Chikashi Toyoshima for the critical proofreading of the manuscript and for the suggested clear differentiation between the hydrophobic binding site of CSs and the assumed hydrophilic receptor site of SOSA in Na+/K+-ATPase. We thank very much Harvey Gonick for his consistent help in improving the manuscript and for the linguistic corrections.

#### **REFERENCES**

**Conflict of Interest Statement:** Dr. Franz Kerek is founder and majority shareholder of the company SiNatur GmbH in Munich, Germany where the R&D activities of the novel factor SOSA were conducted. Prof. Victor A. Voicu has no conflict of interests.

*Received: 03 October 2014; accepted: 16 December 2014; published online: 23 January 2015.*

*Citation: Kerek F and Voicu VA (2015) Spherical oligo-silicic acid SOSA disclosed as possible endogenous digitalis-like factor. Front. Endocrinol. 5:233. doi: 10.3389/fendo.2014.00233*

*This article was submitted to Neuroendocrine Science, a section of the journal Frontiers in Endocrinology.*

*Copyright © 2015 Kerek and Voicu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

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