ISSN Versión Impresa 1816-0719  
ISSN Versión en línea 1994-9073  
ISSN Versión CD ROM 1994-9081  
e Biologist (Lima), 2025, vol. 23 (2), 229-237  
o
VOL. 23.  
N
2, JUL-DIC 2025  
e Biologist (Lima)  
ORIGINAL ARTICLE / ARTÍCULO ORIGINAL  
OPTIMAL AND INTELLIGENT SUBMARINE DESALINATOR BASED ON  
SMART SENSORS BY AMORPHOUS NANOSTRUCTURES  
DESALINIZADOR SUBMARINO ÓPTIMO E INTELIGENTE BASADO EN  
SENSORES “SMART” MEDIANTE NANOESTRUCTURAS AMORFAS  
J. Alan Calderón Ch1,2,3*, Fernando O. Jiménez U.2, Álex J. Quispe M.4, L. Walter  
Utrilla M.4, Dante J. R. Gallo T.5, Wilson Chauca C.2, Diego Saldaña V.2 & Robert W. Castillo A.6  
1
2
3
4
5
6
Applied Physics, Institute for Physics, Technical University of Ilmenau, Ilmenau 98693, Germany.  
Pontificia Universidad Católica del Perú, Mechatronic Master Program and Energy Laboratory, Lima, Perú.  
Aplicaciones Avanzadas en Sistema Mecatrónicos JACH S.A.C.  
Universidad Nacional San Antonio Abad del Cusco, Cusco, Perú.  
Universidad Continental, Huancayo, Perú.  
Universidad Nacional del Callao, U. N. C., Perú.  
* Corresponding author: alan.calderon@pucp.edu.pe  
J. Alan Calderón Ch.: https://orcid.org/0000-0002-6486-5105  
Fernando O. Jiménez U.: https://orcid.org/0000-0003-0540-9481  
Álex J. Quispe M.: https://orcid.org/0000-0002-7788-1403  
L. Walter Utrilla M.: https://orcid.org/0000-0001-8645-7324  
Dante J. R. Gallo T.: https://orcid.org/0009-0004-4506-3205  
Wilson Chauca C.: https://orcid.org/0009-0009-7075-0337  
Diego Saldaña V.: https://orcid.org/0000-0003-2058-747  
Robert W. Castillo A.: https://orcid.org/0009-0002-5258-3319  
ABSTRACT  
In this research, a smart system for a small submarine desalinator is proposed. According to getting optimal desalination  
of seawater that can be adapted and used by many denizens along the coast of Peru, which is located near the Pacific  
Ocean. is desalination submarine was designed using thermodynamic analysis to organize measurements of physical  
variables in seawater and during the desalination process (temperature, position, velocity, force). e desalination system  
was programmed autonomously to make its own decisions based on the physical variables measured by the submarine’s  
sensors, including correlating these variables with theoretical models of chemistry and thermodynamics. ese models,  
in turn, support the development of optimal, adaptive, and predictive systems for desalination. e proposed system can  
operate on the ocean surface and assist communities affected by water scarcity.  
Este artículo es publicado por la revista e Biologist (Lima) de la Facultad de Ciencias Naturales y Matemática, Universidad Nacional Federico Villarreal,  
Lima, Perú. Este es un artículo de acceso abierto, distribuido bajo los términos de la licencia Creative Commons Atribución 4.0 Internacional (CC BY  
4.0) [https:// creativecommons.org/licenses/by/4.0/deed.es] que permite el uso, distribución y reproducción en cualquier medio, siempre que la obra  
original sea debidamente citada de su fuente original.  
DOI: https://doi.org/10.62430/rtb20252322028  
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Keywords: Desaliniator – Nanoestructures – Optimization – Smart sensors  
RESUMEN  
En esta investigación, se propone un sistema inteligente para un pequeño desalinizador submarino. Con el objetivo de  
lograr una desalinización óptima del agua de mar, se propone adaptarlo y utilizarlo en la costa peruana, ubicada cerca del  
océano Pacífico. Este submarino desalinizador fue diseñado con aplicaciones de análisis termodinámico para organizar  
las mediciones de variables físicas del agua de mar y también durante el proceso de desalinización (temperatura, posición,  
velocidad, fuerza). Se programó la autonomía del sistema desalinizador para tomar sus propias decisiones sobre las variables  
físicas medidas por los sensores del submarino, inclusive la correlación de las variables medidas con modelos teóricos de  
química y termodinámica; que a su vez son un soporte para obtener sistemas óptimos, adaptativos y predictivos para la  
tarea de desalinización. El sistema propuesto puede operar sobre la superficie del océano, y ayudar a las comunidades  
humanas afectadas por la carencia de agua.  
Palabras clave: desalinizador – nanoestructuras – optimización – sensores inteligentes  
INTRODUCTION  
e Fig. 1 depicts part of the setup for the desalinization  
physical analysis, in which is represented the desalinator  
system, this is composed by its collector “A” (light blue  
cylinder), also by its depositor “W” (a cone with own  
filter), as well as the main ocean water depositor “B” (the  
bigger cylinder in the system). Hence, the sun “S” heat  
achieved by the desalinator system is the main energy to  
get the water condensation over “A”.  
Desalinator systems are quite necessary for desertic places,  
especially when there are many towns without access to  
get water for their domestic use (Hamed, 2017; Yahui et  
al., 2023; Alenezi & Alabaiadly, 2025).  
Consequently, it was designed an algorithm to simulate  
every response from the designed desalinator system  
(Åström & Hägglund, 2012), such as for example sun  
energy absorbance, temperature, pressure, humidity in  
the thermal chamber, as well as the physical variables from  
the dynamic of the desalinator system that are given by  
volume of condensed water, position, speed and vibration  
(Calderón et al., 2022).  
Moreover, the algorithm designed for the simulations  
was adapted for the experimental analysis to meet  
instrumentation requirements, such as the selected  
microcontroller to control all processes on the designed  
desalinator. It was crucial to recognize that a short response  
time and high robustness of every sensor are necessary. For  
this reason, the targets were achieved due to the sensors  
(designed smart sensors) being based on nanostructures  
(Calderón et al., 2022), this means that the main control  
algorithm of the designed system will get more time to  
execute intricate tasks (Lei et al., 2007) in order to achieve  
an optimal desalinization; ergo, the proposed submarine  
was based on simple and standard designs, but improved  
by advanced sensors due to achieve good performance in  
the navigation because of optimal dynamical analysis as  
it described on paragraphs above (Analog Devices, 1999;  
Ranjna et al., 2023; Yunhwan et al., 2025).  
Figure 1. Scheme of a general desalinator based on  
condensation. W = a cone with own filter. B = the bigger  
cylinder in the system. S = sun. A = water condensation.  
In fact, it was studied and analyzed some techniques  
according to design a mechatronic system to deal with the  
desalinization problem, for which the designed model also  
has the possibility to be a support for users on the sea and  
outside, as well as to support guiding users to find optimal  
roads in the ocean while getting communication with  
different users (inside the sea and outside) in concurrent  
to desalinate water and storing inside itself. Hence, it is an  
objective to propose an analytical mathematical model to  
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Optimal and intelligent submarine desalinator based on smart sensors  
correlate the dynamic system of the mechatronic design,  
a robust energetic system, and an optimal communication  
system, which is explained and detailed in the following  
chapter.  
Whereas, for the context t >= L, it was obtained the  
equation (5).  
( )  
푑푇 푡  
( )  
+ 푇 푡 = 퐾푝 푈 (5)  
Ʈ
푑푡  
Correlating the solutions from previous equations, with the  
Fourier heat transfer model, which is given by the equation  
(6), where “q” is the heat, “k” is the thermal resistivity, and  
“T” is the temperature in dependence on the geometry and  
heat propagation road. Otherwise, it was possible to get  
good estimations of the condensed water mass.  
erefore, in this research are proposed some systems to solve  
this task, which is supported by a previous understanding  
of the problematic “desalinate ocean water for domestic  
use”. us, it was possible to design a mathematical model  
based on the correlation between thermodynamic analysis  
with Modulating Functions according to obtain a robust  
model to describe the desalinization process.  
푞 = −푘 훻푇 (6)  
e equation (7) shows an expanded model from the  
equation (6).  
MATERIALS AND METHODS  
푑푇 푑푇 푑푇  
푞 = −푘 (  
,
)
(7)  
e designed system can be explained by the dynamics  
and thermodynamics of the proposed smart desalinator.  
Most of the measured physical variables are given by first  
order models (at is based on their linear operating work  
chosen for this research), hence, it was necessary to study  
that equations, such as given by the equation (1) in Laplace  
domain “S”, in which “T” is the temperature variable,  
“U” is the input excitation variable, “” is the gain of the  
system (Åström & Hägglund, 2004; Bistak et al., 2023), “”  
is the response time and “” is its time delay (Vajta, 2000;  
Idrees, 2017).  
푑ꢂ 푑ꢃ 푑ꢄ  
erefore, by the described equations above, it is possible  
to estimate the heat achieved by the designed desalinator  
(Medina, 2009). As well as, the following equations propose  
the temperature of the desalinator surface, where it is  
obtained the condensed water after the desalination process.  
e heat model, by heat intensity “I”, in dependence on  
the frequency “w”, speed of light “C”, Planck constant  
“”, universal constant “K”, temperature “T”, which is  
proposed by the equation (8) according to get a general  
model of heat transfer by radiation (Landau & Lifshitz,  
1959; Feynman et al., 1962).  
( )  
푇 푆  
퐾푝  
=
푒−퐿ꢀ (1)ꢀ  
( )  
푈 푆  
Ʈ푆 + 1  
ℏ 푤3  
e following equation (2) is consequently the inverse  
Laplace transformation on the equation (1), in which it is  
considered the function “”, because of the delay analysis.  
In addition that “” helps to get information of the physical  
processes delays during the water desalinization (during the  
condensation process) (Sherman et al., 2023).  
퐼 =  
(8)ꢀ  
ℏꢅ  
ꢆꢇ  
2
2
휋 퐶 (푒  
− 1)  
Furthermore, it is known the equation (9) (Landau &  
Lifshitz, 1959; Feynman et al., 1962).  
∞
푄
ꢈ
=
ꢉ 푑푤  
(9)  
∫
0
푉
( )  
푑푇 푡  
(
)
(
)
(
)
Ʈ
+ 푇 푡 = 퐾푝 푈 푡 − ꢁ ɣ 푡 − ꢁ  
(2)  
From the equation (8), it is proposed the equation (10).  
푑푡  
ℏꢅ  
For the operating work of the time “t” between 0 to “L”,  
it is proposed the equation (3), which is the result from  
the equation (2).  
ꢂ =  
(10)  
ꢆꢇ  
It is achieved the equation (11) from the equation (10).  
( )  
푑푇 푡  
ꢆꢇ  
( )  
+ 푇 푡 = 0 (3)  
Ʈ
푑ꢂ = 푑푤  
(11)  
푑푡  
ℏ
In the context that “U” is zero under its time domain,  
because the system is not under its stimulation, such as a  
consequence the previous equation (3) is reduced to the  
equation (4).  
e equation (12) is achieved replacing the equation (8)  
in the equation (9).  
∞
ꢊ
ꢋ
ℏ 푤3  
= ∫  
푑푤 (12)ꢀ  
ℏꢅ  
ꢆꢇ  
0
2
3
휋 퐶 (푒  
− 1)  
( )  
푇 푡 = 푇0 (4)  
From equations (10) and (11) in equation (12), it is  
obtained the equation (13).  
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In fact, it was obtained the equation (24).  
∞
ꢊ
ꢋ
ꢂ3  
푒푥 − 1  
∞
= 푎 ∫  
푑ꢂ (13)ꢀ  
∫ (−푥)(−푥)(−푥) 푒−푛ꢀ푑푥 = −1 (−2)(−3)ꢁ  
(24)ꢀ  
−3  
(
)
0
0
In which, it is proposed the equation (14).  
As well as, it was reduced to the equation (25).  
4
(
)
퐾푇  
∞
푎 =  
(14)ꢀ  
∫ 푥3 푒−푛ꢀ푑푥 = 6ꢁ−4 (25)ꢀ  
0
휋2(ℏ퐶)3  
erefore, from the equation (13), it is organized the  
equation (15).  
Equation (25) in (18), it was obtained the equation (26).  
∞
푄
푉
6
ꢁ4  
∞
푄
푥
−1  
(
)
= 푎  
ꢂ3  
푒
− 1  
푑ꢂ  
(15)  
∫
= 푎 ∑  
(26)ꢀ  
0
푉
푛=1  
From the equation (15), it is expanded to obtain the  
equation (16).  
en, the equation (27) is consequently from the equation  
(26)  
∞
푄
푄
1
1
1
= 푎 ∫ 푥3 (푒−1ꢀ + 푒−2ꢀ + 푒−3ꢀ + ⋯ ) 푑푥 (16)ꢀ  
= 6푎(1 +  
+
+
+ ⋯ ) (27)ꢀ  
푉
0
푉
22 32 42  
Moreover, the equation (16) is reduced to the equation  
(17).  
Even though, the equation (28) generalizes a model  
to achieve the heat by radiation that was useful to get  
condensed water from the desalinator system that was  
designed for this research (Landau & Lifshitz, 1959;  
Feynman et al., 1962).  
∞
∞
푄
= 푎 ∫ 푥3 ∑ 푒−푛ꢀ 푑푥 (17)ꢀ  
푉
0
푛=0  
Nevertheless, it was organized the equation (18) from  
the previous equation (17) (Landau & Lifshitz, 1959;  
Feynman et al., 1962).  
4
( )  
퐾ꢀ  
2
푄
6
∞
푛=1  
∑
=
(28)  
4
3
(ℏ퐶)  
푉
푛
휋
∞
As well as, the dynamic model (mathematical analysis)  
for the desalinator system was determined by generalized  
Lagrange that is given by the following equation.  
∞
푄
= 푎 ∑ ∫ 푥3푒−푛ꢀ 푑푥  
(18)ꢀ  
푉
0
푛=1  
As well as, it is known the equation (19) in order to reduce  
the equation (18).  
푑
휕ꢂ  
휕ꢂ  
휕ꢄ  
(
) = (  
)
(29)  
푑ꢁ 휕ꢃ  
∞
1
1
1
In which “L” depends on the kinetic and potential energy  
of the main system (desalinator), in spite of it was possible  
to find solutions through the correlation of the desalinator  
dynamics with the thermodynamics effects. Hence, in the  
Fig. 2, it is depicted the desalinator model prototype of  
this research “DS”.  
∫ 푒−푛ꢀ 푑푥 = −  
0
(
−
)
(19)ꢀ  
ꢁ
푒∞ 푒0  
erefore, it was achieved the equation (20).  
∞
∫ 푒−푛ꢀ 푑푥 = ꢁ−1 (20)ꢀ  
0
Derivative by “n” on the equation (20), it was obtained  
the equation (21).  
e data communication is received and transmitted  
through its antenna “A”, the main control algorithm  
executed by the controller “C”, the sensor system for this  
desalinator designed is represented by “S”, the condensed  
water chamber “T”, the power subsystem “A”, where are  
stored the rechargeable batteries (hybrid model due to  
part of them are composed with small batteries based on  
nanostructures for this research), even though part of the  
recharged energy was achieved by small sun panels (based  
on nanostructures) during the condensed process; therefore,  
there were controlled the propellers “P1 and P2” owing to  
get a controlled movement for the desalinator prototype  
also in the ocean (Wang et al., 2021).  
∞
푑
푑
(∫ 푒−푛ꢀ 푑푥) =  
0
ꢁ
(21)ꢀ  
−1  
(
)
푑ꢁ  
푑ꢁ  
Derivative by “n” again to achieve the equation (22).  
∞
푑
푑
(∫ (−푥) 푒−푛ꢀ푑푥) =  
0
( −1 ꢁ  
)
(22)ꢀ  
−2  
(
)
푑ꢁ  
푑ꢁ  
Derivative by “n” one more time again, according to achieve  
the equation (23).  
∞
푑
푑
(∫ (−푥)(−푥) 푒−푛ꢀ푑푥) =  
0
( −1 (−2)ꢁ  
)
(23)ꢀ  
−3  
(
)
푑ꢁ  
푑ꢁ  
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Figure 2. Desalinator prototype scheme. DS = Desalinator model prototype of this research. A = antenna. C = controller.  
S = sensor system. T = condensed water chamber. P1 and P2 = propellers.  
It was analized a second order differential equation for  
the dynamic interpretation of the desalinator, which is  
nonlinear model (Medina, 2010). Notwithstanding, from  
the second order differential equation (30) it was deduced  
the nonlinear model by the coefficients “a , a2, a3, and b ”  
and response variable “y(t)” depending on i1ts input variab1le  
“u(t)” (Santana, 2023; Strang & Herman, 2025).  
e equation (35) is a reduction from the equation (34).  
( )  
푎1 ꢅ푣 푡  
(
)
−
= ꢅ푡  
35 ꢀ  
ℎ
푣2  
Looking for the integral in the equation (35), it was  
obtained the equation (36).  
ꢇ
ꢇ
( )  
ꢅ푣 푡  
푣2  
푎1  
ℎ
−
∫
= ∫ ꢅ푡  
(36)ꢀ  
ꢅ2푦(푡)  
ꢅ푡2  
ꢅ푦 푡  
ꢅ푡  
ꢇ
0
ꢇ
0
( )  
( )  
+ 푎3푦 푡 = 푏1푢(푡)  
(
)
30  
푎1  
+ 푎2  
Hence, the equation (37) is the reduction of the equation  
(36).  
For which, the coefficient “a2” was obtained in the static  
response analysis that is given by the equation (31), where  
this coefficient got proportionality in dependence on “h”.  
푎1  
ℎ
1
1
(
−
) = 푡 − 푡0  
(37)ꢀ  
푣
푣0  
From which, it was obtained the equation (38), where  
the initial speed “” of the desalinator designed is not null,  
however, the equation helps to validate the circumstances  
when it returns to get static equilibrium.  
( )  
푑ꢆ ꢁ  
푎2 = ℎ  
(31)  
푑ꢁ  
ere were not deformations on the main system and  
there is considered an impulse as the main input excitation  
signal during a short proposed steady state, therefore it was  
achieved the following equation (32).  
1
푣 =  
(38)ꢀ  
ℎ
푎1  
1
푣0  
(
)
푡 − 푡0  
+
2
ꢅ2푦(푡)  
ꢅ푡2  
ꢅ푦 푡  
ꢅ푡  
( )  
Furthermore, from the equation (38), it was possible  
to deduce the equation (39) according to estimate the  
maximal distance traveled till the desalinator will get its  
static equilibrium again.  
(
)
푎1  
+ ℎ (  
)
= 0  
32 ꢀ  
As well as, it was proposed the equation (33).  
( )  
푑ꢆ ꢁ  
ꢇ
ꢇ
ꢅ푡  
푣 =  
(33)  
푑ꢁ  
∫ ꢅ푦 = ∫  
ꢇ
0
(39)  
ℎ
1
ꢇ
0 푎1  
(
)
푡 − 푡0  
+
푣0  
Hence, the equation (34) was obtained as a consequence  
to replace the equation (33) in the equation (32).  
erefore, it was analyzed and detailed the problematic to  
achieve an advanced and robust mathematical model to  
design a mechatronic system to be a support for users as  
desalinator and support on the ocean and outside.  
푎1  
ꢅ푣(푡) = −푣2 ꢅ푡  
34 ꢀ  
(
)
ℎ
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However, it was necessary to evaluate the performance of  
the designed mathematical models (which were studied by  
equations described in paragraphs above) by simulations  
and experimental results. It means, in the following chapter  
are described results of the adaptive algorithms designed for  
the simulations results and the consequence applications  
of the experiments based on the hardware mechatronic  
design of the proposed desalinator.  
Watts) during 60 minutes of heat absorbance, in order to  
obtain approximately 225 mL of condensed water, which  
is showed on the Fig. 3. Hence, the proposed desalinator  
system, which looks like a small submarine optimized its  
advanced control system because of its dynamic response on  
the sea and outside, as well as its optimal thermodynamic  
response helped to condensate sea water in concurrent  
with other tasks that the desalinator was solving, such  
as for example its internal communication systems by  
Infrared (IR) or external communication with users by  
Radio Frequency (RF).  
Ethic aspects: is article has not ethical conflicts in the  
proposed research, which was cited every bibliographic  
reference for every analysis described.  
e condensation sea water needed a sophisticated  
coordination analysis between the control activities of  
the desalinator system with the integration of smart sensors  
prepared for the thermal responses/tasks, moreover the  
strategical characteristics of the desalinator system based  
on the maximal possibilities to get sun energy over the  
condensation cabin with the optimal heat absorbance,  
which depended on the nano materials covering over the  
condensation cabin.  
RESULTS  
e results achieved in this research are focused on the  
thermal effects of the designed desalinator, aside from  
its dynamic performance, it means that the desalinator  
system needed maximal 25 Watts (average around 10  
Figure 3. Heat and temperature inside the designed desalinator system.  
erefore, the heat absorbance by radiation, the optimal  
measured variables of humidity, sea water level, temperature  
by sensors based on nanostructures that were designed for  
this desalinator, supported to achieve optimal condensation  
due to short response time of the sensors based on  
nanostructures, which also was correlated with faster data  
communication (by wireless, using IR) between internal  
modules in the desalinator system.  
In fact, the capacity to organize the internal process of  
the small submarine coordinated with the smart sensors  
based on nanostructures gave the chance that the main  
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Optimal and intelligent submarine desalinator based on smart sensors  
control system could not be saturated by redundant tasks  
(because the smart sensors reduce the processing task), then  
the condensation process was enough optimized by the  
main control system, by the smart sensors and the optimal  
covering nano materials to improve the heat absorbance.  
For this reason, the water condensed volume also tended  
to get linear response on dependence of the geometrical  
proportion of the designed desalinator.  
physical parameters for the optimal displacement of the  
desalinator system depending on the correlation between  
dynamic physical laws of the small submarine with  
Modulating Functions parameters achieved online, which  
was a good information proportionate optimal trajectories  
for the submarine.  
e Fig. 4, shows the response variables “speed and  
position” of the designed desalinator, while it is moving  
inside and outside the sea. e previous equations above  
helped to design the adaptive algorithms to get optimal  
estimations of the main physical variables to correlate the  
condensation task with the desalinator displacements on  
the sea, as well as for outside tasks.  
For the dynamical analysis results of the designed  
desalinator, it was necessary to coordinate the integration  
activities between the smart sensors based on nanostructures  
to measure its displacement, its impulse force, its speed.  
Hence, the main control system identified online the  
Figure 4. Response variables of the desalinator dynamics.  
DISCUSSION  
For the context of the designed position sensors, it was  
worked by the IR sensors, which helped to get estimations  
of bodies around the designed submarine, however, there  
were problems during the optimal movement when  
the IR signal did not find appropriated surfaces for its  
optimal distance estimation, hence, it was complemented  
the distance detection by ultrasound sensors based on  
nanostructures (Yoon et al., 2022).  
It had been designed a small automate that can displace  
itself inside the sea, by the dynamical analysis of the external  
forces around it. e designed system has the possibility  
to get smart responses due to most of its physical variables  
are measured by sensors based on nanostructures, it means  
there were obtained short response time and high robustness  
in comparison of the traditional electromechanical sensors  
(Texas Instruments, 2017). Nevertheless, the operating work  
for every physical variable was not enough according to  
evaluate the performance of the designed smart submarine  
under longer distances (Nafey & Safwat, 1988).  
In fact, the communication between the submarine  
prototype with the external medium, as well as receiving  
orders/tasks by the main user was optimal under delimitated  
range of work, which depends on faster and robust response  
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e Biologist Vol. 23, N 2, jul - dec 2025  
Calderón et al.  
from its sensors, that is quite important due to coordinate  
the main task of the smart submarine owing to be helper  
for the external user, such as for example to give the  
ubication and image information inside the sea (Bistak  
et al., 2023). It means, the optimal coordination between  
internal modules of the designed desalinator with its smart  
sensors based on nanostructures, moreover with nano  
covering material to improve heat absorbance, also a good  
energy and communication system (by IR and RF) got as  
a consequence a sophisticated desalinator that achieves sea  
water condensation and optimal movement on the sea and  
outside owing to be a good support for users.  
DJRGT = Dante J. R. Gallo T.  
WChC = Wison Chauca C.  
DSV = Diego Saldaña V.  
RWCA = Roberto W. Castillo A.  
Conceptualization: JACCh  
Data curation: JACCh, DJRGT, WChC, DSV  
Formal Analysis: JACCh, FOJU, DJRGT, DSV  
Funding acquisition: JACCh, AJQM, LWUM, DSV,  
RWCA  
Investigation: JACCh, FOJU, AJQM, LWUM, WChC, DSV  
Methodology: JACCh, FOJU, DJRGT, DSV, RWCA  
Project administration: JACCh, FOJU, DJRGT, RWCA  
Resources: JACCh, AJQM, LWUM, WChC, DSV, RWCA  
Software: JACCh  
ACKNOWLEDGEMENT  
It is expressed gratitude to colleagues from the  
nanostructures researching group of the Technische  
Universität Ilmenau, TUI, Deutschland, owing to their  
shared teachings during the posgrade time. It is expressed  
special thanks for Mr. Genaro Chauca Jimenez and Mrs.  
Luisa Cordova Sallo due to their financial support to the  
development of the proposed article. It is expressed warm  
thankful to Mr. Miguel A. Badillo B., Mr. Bryan C. Bastidas  
R., Mr. Michael G. Ramirez M., Mr. César F. Pinglo A.,  
Mr. Brandon A. Polo V., and Mr. Jose Espettia due to their  
support in the feedback analysis of the article, as well as  
the development of some prototypes for the experiments.  
It is expressed special thankful for the companies  
“MERQUITEX S.A.C”. and “PROYINOX S.A.C.” owing  
to their support for the development prototypes, which  
were quite important to validate the theoretical models also  
discussed in this proposed research. It is expressed special  
thankful for the companies “OPEN 3D, LABORATORIO  
DE MANUFACTURA DIGITAL” and “GICA E. I. R. L.”  
because of their support for the development prototypes,  
which was based in the support to prepare the experimental  
setup. It is expressed special grateful for the Laboratory of  
Design of the PUCP: Applied Mechanics, Oleo-hydraulics  
and Pneumatics, because of proportioning part of the place  
to evaluate part of the experimental analysis described in  
the presented research.  
Supervision: JACCh, AJQM, LWUM, WChC, RWCA  
Validation: JACCh, FOJU, LWUM, WChC, DSV  
Visualization: JACCh, DJRGT, WChC, RWCA  
Writing – original draft: JACCh  
Writing – review & editing: JACCh, AJQM, LWUM, DSV  
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Received July 8, 2025.  
Accepted October 31, 2025.  
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