Strategies for autonomous work in Peruvian university students
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PAIDEIA XXI
Vol. 13, Nº 1, Lima, enero-junio 2023, pp. 85-102
ISSN Versión Impresa: 2221-7770; ISSN Versión Electrónica: 2519-5700
ORIGINAL ARTICLE / ARTÍCULO ORIGINAL
ANALYTICAL SUGGESTIONS FOR THE
MANUFACTURING OF SMART SENSORS BASED ON
AMORPHOUS NANOSTRUCTURES, UNDER THE ANDES
MOUNTAINS CONDITIONS
SUGERENCIAS ANALÍTICAS PARA LA FABRICACIÓN
DE SENSORES INTELIGENTES BASADOS EN
NANOESTRUCTURAS AMORFAS, ANTE LAS
CONDICIONES DE LAS MONTAÑAS ANDINAS
ABSTRACT
doi:10.31381/paideiaxxi.v13i1.5696
http://revistas.urp.edu.pe/index.php/Paideia
J. Alan Calderón-Chavarri1,2,3*; Eliseo Benjamín Barriga-Gamarra2; Julio César Ta-
fur-Sotelo2; John Hugo Lozano-Jáuregui2,4,5; Hugo Rosulo Lozano-Núñez6; Álex John
Quispe-Mescco7 & Fredy Alan Ccarita-Cruz2
1 Applied Physics, Institute for Physics, Technical University of Ilmenau, Ilmenau 98693,
Germany.
2 Ponti cia Universidad Católica del Perú, Mechatronic Master Program and Energy Laborato-
ry, Lima 32, Perú.
3 Aplicaciones Avanzadas en Sistema Mecatrónicos JACH S.A.C.
4 Universidad Continental, Huancayo, Perú.
5 Northen (Artic) Federal University named after MV Lomonosov, Russian Federation.
6 Universidad del Centro del Perú, Huancayo, Perú.
7 Universidad Nacional San Antonio Abad del Cusco, Cusco, Perú.
* Corresponding author: alan.calderon@pucp.edu.pe
Jesús Alan Calderón-Chavarri: https://orcid.org/0000-0002-6486-5105
Eliseo Benjamín Barriga-Gamarra: https://orcid.org/0000-0002-7781-6177
Julio César Tafur-Sotelo: https://orcid.org/0000-0003-3415-1969
John Hugo Lozano-Jáuregui: https://orcid.org/0000-0002-8430-9480
Hugo Rósulo Lozano-Núñez: https://orcid.org/0000-0003-3264-2908
Álex John Quispe-Mescco: https://orcid.org/0000-0002-7788-1403
Fredy Alan Ccarita-Cruz: https://orcid.org/0000-0001-5724-4864
Este artículo es publicado por la revista Paideia XXI de la Escuela de posgrado (EPG), Universidad Ricardo Palma,
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.
It is proposed in this abstract some suggestions for the manufacturing of
advanced sensors (smart sensors) that are based on amorphous nanostructures
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to prepare intelligent sensors from which their transducer elaboration needs
specic requirements, such as in geometry and material of every sample.
Moreover, it is quite important the correlation understanding between the
necessities of the community or company that will operate with them. It means
the manufacturing of the transducer samples can be prepared by a sputtering
process, atomic load deposition, and also by electrochemical reactions. Hence
it is suggested to analyze the chemical components that are possible to nd in
the Andes mountains, also the strict compromise of the responsible residual
collection of every production step and caring for the environmental conditions.
Furthermore, it is proposed that designers could get an understanding of
the Andes mountains conditions because many times it is not analyzed the
geographic or climatic conditions, where there will be used devices that require
sensors for many applications such as shing tasks, agriculture tasks, mining
tasks, and public transport tasks. In this context, the advantages of the sensors
based on nanostructures are supported by the robustness and short response
time that give more time for active applications of the sensors as part of a
mechanic or mechatronic systems. This advantage helps to program possibilities
by a microcontroller to execute adaptive algorithms and enhance the physical
measurement tasks.
Keywords: Amorphous nanostructures – geographic/climatic Andes moun-
tains conditions – smart sensors
En esta propuesta se plantean algunas sugerencias para la fabricación
de sensores inteligentes basados en nanoestructuras amorfas, para lo cual
la elaboración de los transductores de los sensores debe tener en cuenta la
correlación entre su geometría interna (en escala nanométrica) y el material que
lo compone, además de los requerimientos de la empresa que los necesite y el
espacio geográco de la comunidad donde estos sean usados. Tal es así, que la
fabricación de estos sensores requiere procesos complejos cual pulverización
catódica, deposición atómica, e incluso deposición atómica mediante reacciones
electro-químicas. Por lo tanto, se sugiere estudiar qué minerales y componentes
químicos se pueden encontrar en los alrededores de las montañas Andinas,
para así poderlos usar en la fabricación de los sensores, teniendo un estricto
compromiso del cuidado ambiental con los residuos acabado los procesos
de fabricación. Además, se plantea tomar en cuenta la comprensión de las
características geográcas y climatológicas del lugar de fabricación, cual
también el lugar donde se someterá a prueba y uso de los sensores diseñados,
que generalmente para las actividades de la población Andina, puede darse en la
minería, pesca, agricultura y transporte público. En este contexto, los sensores
diseñados en base a nano estructuras tienen la ventaja de un corto tiempo de
RESUMEN
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respuesta y robustez frente a perturbaciones, lo cual es muy útil para tareas
de sistemas mecánicos o mecatrónicas donde el carácter activo de los sensores
mejore el performance de las tareas, también esta ventaja es un soporte desde
el punto de vista de la programación del microcontrolador que le dé el carácter
de inteligencia articial al sensor elaborado, pues permite ejecutar algoritmos
complejos y adaptativos para trabajar en mejor respuesta de la transducción.
Palabras clave: Condiciones geográcas/climáticas Andinas – nanoestructu-
ras amorfas – Sensores inteligentes
INTRODUCTION
The elaboration of advanced
sensors needs very specic conditions,
such as in the cleaning transducers
samples task, which is given by
electropolishing (electrochemical clea-
ning). Also for the elaboration of every
Ultra-Thin Alumina Membrane (UTAM)
sample is needed anodization as well
as to start the procedure of the base,
over which there will be stored atoms
as the dependence on their materials:
titanium, gold, silver, carbon and by
structures: nanotubes, nanowires,
nanodots, etc. (Ljung, 1994; Lei & Cai,
2006; Calderón et al., 2019; Calderón
et al., 2022).
The procedure described above
is expensive, because of all the
needed equipment, as for example,
high vacuum chambers according to
prepare the atomic load deposition,
even though it can be possible to use
electrochemical procedure to obtain
the nanostructures. Hence, it must
be analyzed the procedure for the
responsible residual collections, as a
result it must be analyzed the budget
and the responsibility of the chemical
residual collection in order to not
damage the environment conditions, if
it is decided to produce nanostructures
in industrial level. In fact, it is
suggested to keep good understanding
of the sensors design requirements,
because the dependence with the
geographic/climate effects (Hwang,
2014; Chang et al., 2021; Zhang et al.,
2022; Kees & Kasper, 2023).
Furthermore, in this research there
are proposed some suggestions for the
smart sensors design and the mathe-
matical procedure for the algorithms
to be executed by the microcontroller
of the designed smart sensors. Espe-
cially, there are proposed some sug-
gestions of the applications for the
designed sensor, such as for example,
there were given by the modular sys-
tems to be used for the enhancement
of combustion motors, as a proposal
in this research (Lobnik et al., 2010;
Rahman et al., 2014; Sonker et al.,
2022).
The main objective in this
research is given by the suggested
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procedure for the smart sensors
manufacturing, which also is based
on the transducers that were designed
by amorphous nanostructures. -There
are consequently specic objectives,
such as the transducers design as
the dependence on the transduction
properties. -Besides, it is as a specic
objective the non-linear mathematical
analysis for the adaptive algorithms
design in order to give optimal
measurements. There is another
specic objective is given in explained
suggestions for the applications of the
designed sensors in tasks, such as in
agriculture, mining, shing and public
transport for which it was interpreted
some consequences to be used in the
public transport applications of the
Andes.
MATERIALS AND METHODS
For the advanced smart sensors
that were designed it is proposed to
use aluminum and consequently, it is
possible to prepare different geometries
on nanostructures that could be based
on electropolishing, anodization and
atomic load deposition (Lei & Cai, 2006).
After to have prepared the samples,
it is proposed the transduction design
as dependence on the physical
variable to measure. As for example,
the measurement of ow vibration
surfaces, such as it is depicted by the
gure 1, in which the electromagnetic
transmitter sends a signal in Infrared
(IR) wavelength according to take
information of the target measurement,
which is a vibration surface that
achieve the estimation of the physical
variables of the surface target by an
analysis of the frequencies between
the IR wave with the vibrating surface
frequency. Therefore, the IR receiver
has quite important task to get the
transduction of the IR wave. The
transduced physical variable can be
voltage, electrical current or electrical
resistance represented by the resultant
measured physical variable.
Figure 1. Proposed scheme of the IR receiver/transmitter from
the designed smart sensor.
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On the other hand, it is depicted
the vibration transducer (by contact
between the transducer sample with
the target measurement that also can
be gases), which has the scheme of
an inverted pendulum that was based
on nanoparticles that were xed over
nanotubes and nally, the transduc-
ed resultant physical variable can give
information of the main target as gas
temperature, gas pressure or air hu-
midity. This depicted sensor is showed
by the gure 2.
Figure 2. Proposed scheme of the vibrating transducer from
the smart designed sensor.
Otherwise, the methodology to
suggest the manufacturing of smart
advanced sensors under the Andes
mountains conditions is given by
the recognizement of the geographic
and climate conditions of the place,
in which samples of the advanced
sensors will be created the and also of
the place, where they will be used.
Of course, a simple answer is given
through the protocols of the laborato-
ry, in which will be prepared the nano-
structures samples. Nevertheless, it is
assumed that the laboratory must to
keep the conditions whereas it must
be analyzed the residual reactions,
according to not damage the environ-
ment conditions. It is necessary to
know, what minerals can be possible
to nd in mining around the laborato-
ry, because to be used in the materi-
al analysis of the transducer samples
elaboration.
Furthermore, while it is known the
requirement for which will be used
the sensor, it is necessary to know
if the adaptive algorithm detects the
external parameters and no matter
where to use the designed sensor that
was based in the adaptive properties.
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Notwithstanding, the manufacturing
of the advanced sensor can contribute
to the development of the community
by the engineering applications.
The next step of the proposed meth-
odology is based on the understand-
ing of the theory and practice for the
advanced sensors design. Therefore,
it is proposed to start the theoretical
analysis by the Schrödinger equation
as it is showed by the equation (1), in
which “h” is the Planck constant, “ψ”
is the wave function for the particle or
molecule in the position “x” and time
“t”, and “m” is the mass. The equa-
tion (1) is a non-relativistic model of
the Schrödinger equation (Wichmann,
1971).
The equation (2) is another pre-
sentation of the non-relativistic
Schrödinger equation that gives pre-
sentation of its wave behavior (Wich-
mann, 1971).
One solution is obtained by the
equation (3), in which “E” is the energy
level of the particle (Wichmann, 1971).
As well as “ϕ”(x) is the amplitude
that depends on “m”, “h”, “E”, and “V”
that is the achieved potential when
it is analyzed the voltage effect of the
charged particle in the equation (2).
This dependence can be found by the
function “f” as the behavior of a par-
ticle or molecule in the Schrödinger
equation. Therefore, the amplitude
“ϕ”(x) is given by the equation (4).
Furthermore, the potential function
is also in dependence on “m”, “h”, and
“E”, by the function “g” that also can
be found the behavior of a charged
particle or molecule in the Schrödinger
model of the equation (2). Hence, the
potential is given by the equation (5).
Manufacturing of smart sensors
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As a consequence, the Schrödinger
analysis that was described in para-
graphs above can help to understand
the wave-particle behavior of the inter-
action between the particles-molecules
that are part of the target measure-
ment system, such as the task to mea-
sure pressure, temperature, volume,
ow of gases, liquids or solids, with
the particles-molecules of the con-
tact surface of the transducer sample.
This interaction is quite important to
research, in order to get good under-
standing of an optimal transduction
for the physical variables measure-
ment of the designed smart sensor, for
which the Schrödinger equation gave
support in order to prevent the nature
of the particle as a wave or as a par-
ticle as part of the interaction. Hence
the non-relativistic Schrödinger equa-
tion was based in not fast movement
of the particles in interaction (not so
proximal to the speed of light) is the
theoretical model to get information
of the transduction. Nevertheless, it
must be analyzed cases of relativity,
while it is working with non-contact
physical variables measurement, be-
cause the interaction is based in the
package of information that is ob-
tained by the selected electromagnetic
wave that gives the information of the
physical variable under the transduc-
tion effect (Wichmann, 1971).
Whereas the main task of the de-
signed smart sensor is the measure-
ment of the target object physical vari-
ables by short response time and high
robustness, which is achieved because
of the amorphous nanostructures as
part of the designed transducer sam-
ple. Consequently, it is possible to use
a microcontroller to execute advanced
and adaptive algorithms to give opti-
mal measured data due to the short
response time of the measurement
that can give time to execute the adap-
tive algorithms for the optimal mea-
surement.
In this research, it was used a
polynomial analysis in order to get a
mathematical model for the analysis
and interpretation of the data that was
achieved from the experiments (mea-
surements), which is based on “Mod-
ulating Functions” owing to get the
correlation among the measurement
information with a theoretical model
to get an optimal estimation of physi-
cal variables complementing the main
measurement. The modulating func-
tion model is given by the equation (6),
in which “y(t)” is the output variable
as an array or a matrix of output vari-
ables, “u(t)” is the input variable as an
array or a matrix of input variables,
the derivatives can be generalized to
order “n”, because to nd the appro-
priated polynomial model and as a
consequence of every coefcient “a”
and “b” that by the auxiliary variable
“i” can x matrixes of parameters for
the “a” and “b” respectively (Pearson,
1995).
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Therefore, the error model in or-
der to achieve the parameters of the
mathematical model is obtained by in-
troducing a modulated function “” in
the main model, as for example, in a
second order model that is proposed
by the equation (7), as well as the error
in the proposed analysis is obtained
by the integration in the domain from
0 to a time parameter “T” (Pearson,
1995).
According to keep the error model,
there are proposed the following equa-
tions (8) and (9) as the part of equation
above (Pearson, 1995).
By integral analysis solutions, the
equations (8) and (9) are reduced to
the following equations (10) and (11)
(Pearson, 1995).
For which the Boundary Condition
(BC) is given by the equation (12) that
is obtained from the equations (10)
(11)
(17)
and (11) described above (Pearson,
1995).
(11)
(17)
Therefore, reorganizing the equa-
tion (7), it is achieved the equation (13)
(Pearson, 1995).
(11)
(17)
For which, replacing the equations
(10), (11) and (12) in the equation (13),
it is obtained the equation (14) (Pear-
son, 1995).
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Whereas, if the BC gets null value,
as an assumption for the nal solu-
tion of the equation (14), it can be pro-
posed the following equation (15) that
gives information of the identied pa-
rameter of the main measured system
with the solved correlation between
the modulated function with the re-
sponse variable according to get an
error equation (Pearson, 1995).
(11)
(17)
(11)
(17)
By other side, also it is possible
to nd the parameters by the matrix
“
θ
”, which is proposed in the equation
(16), because of costing function anal-
ysis (Wang, 2009).
Hence, e2 is the costing function
“J” that is given by the equation (17)
(Wang, 2009).
(11)
(17)
The equation (18) is an expansion
(11)
(17)
of the equation (17) (Wang, 2009).
(11)
(17)
For the context
equal to zero, it
is possible to obtain the parameters
“
θ
” of the polynomial model, which is
given by the equation (19) (Wang, 200
However, the Least Mean Square
(LMS) analysis helps to get an adap-
tive ltering for every input signal, as
well as some estimations for expected
physical variables in the measurement
process. In the gure 3 it is depicted
the curve “C”, which can be given from
data experiment, the straights “L1”
and “L2” proportionate information of
the relation between the costing func-
tion with the weight matrix by a recur-
sive criterium (Calderón et al., 2019,
2022).
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PAIDEIA XXI
From the gure above, it is obtained
the tangent of the angle “α”, in depen-
dence of the costing functions “J” for
the positions 1 and 2, as well as in de-
(11)
(17)
Figure 3. Geometric representation of the adaptive algorithm by LMS analysis.
pendence on the weights “W” for the
positions “m” and “m+1”, which is pro-
posed by the equation (20) (Calderón
et al., 2019, 2022).
From the previous equation, the
difference of the both costing func-
tions values is given by the error
square “e2”, which is proposed in the
following equation (21) (Calderón et
al., 2019, 2022).
(11)
(17)
On the other hand, the previous
equation can be reduced to the point
“A” analysis by derivative of the er-
ror square depending the weight “W”
and the adjusted coefcient
σ
, which
is proposed on the following equation
(22). (Calderón et al., 2019, 2022).
Hence, looking for a reduction of
the equation (22) to an innitive point
of the curve “C” (gure 3), such as it is
(11)
(17)
given in the point “A” by a derivative
strategy it is obtained the equation
(23) (Calderón et al., 2019, 2022).
(11)
(17)
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In the equation (24) it is continued
the derivative analysis, but it is re-
placed the error “e” by the difference
among the desired signal “D” with the
estimated signal “XW” that has the de-
pendence on the internal variable “X”
with the weight matrix “W” (Calderón
et al., 2019, 2022).
(11)
(17)
Finally, it is possible to achieve
the recursive equation known as LMS
algorithm by the equation (25), in
which the adjusted coefcient “
µ
” is a
function of the previous coefcient
σ
(Calderón et al., 2019, 2022).
(11)
(17)
Moreover, the adapted response
by LMS is given by the equation (26)
(Calderón et al. 2019, 2022).
(11)
(17)
Indeed, the manufacturing of smart
sensors based in nanostructures
needs a good understanding of the
requirements of the target sensor
to elaborate, such as for example
to know, what physical variable will
measure the designed sensor or what
material and geometry to use in the
sample transducers of the designed
sensor. Also to know, whether the
laboratory is localized near a mining
according to provide some minerals
that can be useful for the transducer
composition to elaborate. As well as
to know the geographic and climate
conditions of the place, where is
localized the laboratory, because of
to know what external disturbances
could be possible to appear during
the measurements and where to
store the residuals components that
were achieved during the sensors
elaboration in order to care the
environment condition by caring
the residual components evacuation
after the reactions of the sensors
manufacturing, that means the
result of all the elaboration process
of smart sensors that were based in
nanostructures.
The paragraph above is depicted by
the two blue arrows in the entrance
and exit of the back square in the
gure 4. Otherwise, the two red
arrows at the entrance and exit of
the blue box inside the back square
represent the measured data and
the adaptive/ltered/estimated data
that were achieved by the designed
smart sensor. Inside the blue box is
depicted the procedure that made
by the microcontroller according to
process the information from the
transducers that are in interaction
with the target to measure its physical
variables (contact measurement).
Either, another option to measure is
given through the non-contact with
the target to measure because of there
is an electromagnetic wave to get the
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PAIDEIA XXI
information of the physical variables
to measure from the target. For both
contexts that are described above, it
is executed an adaptive algorithm that
was based in modulating functions
for the polynomial analysis of the
transduced data and previous ltered
by LMS, taking reference a theoretical
model of the physical variable to look
for. Moreover, the algorithm has in
reference the Schrödinger analysis in
order to take care the transduction
effect by the interaction between
the target to make measurements
as the dependence on the particles
interaction in nanoscale, such as the
wave-particle behavior in atomic scale
around. Therefore, the measured data
as a consequence of the designed
smart sensor can execute complicated
algorithms, because of the short
response time and high robustness
from the designed transducers
that were based in amorphous
nanostructures. (Calderón et al.,
2019, 2022).
Figure 4. Scheme representation of the proposed methodology for the
smart sensors manufacturing.
Ethical aspects: This research has
not ethical conicts in the proposed
article, it was cited every bibliographic
reference for every described analysis.
RESULTS AND DISCUSSION
The result analysis of the proposed
research is based in the simulation
and experimental analysis results
as a consequence of the previous
mathematical analysis explained in
chapters above. For the simulation
analysis, it was simulated a second
order system in order to explain an
ideal signal to follow by the adaptive
models, even though this model gives
a theoretical criterion of the referential
signal for the adaptive measurements.
Therefore, the adaptive model can keep
in reference an ideal model (supported
by theoretical analysis) even though
many times it is not so simple to nd a
theoretical model according to keep it
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as a reference. Hence, in this context
it is necessary to keep as a reference a
ltered input signal, while it is known
an estimation of the added external
noise (Lei & Cai, 2006).
Otherwise, it is necessary to know
that the parameters of the designed
transducer can be the correlation
between the system in its nanoscale
(as the dependence on its geometry
and materials) with the external macro
system that is given by the transducer
in fact. In summary, this process
is executed by the algorithm of the
microcontroller of the designed sensor
that is represented by the owchart of
the gure 5.
Figure 5. Flowchart of the algorithm designed for the
microcontroller of the smart sensor.
There are quite important characte-
ristics (geometrical and material) that
recognized the designed transducer as
part of the designed sensor (De Berre-
do-Peixoto et al., 2010). The transduc-
tion task is given by the interaction
effect between the target to measu-
re its physical variables, such as for
example to measure the temperature,
pressure, vibration, distance as the
dependence of the object to measu-
re with the designed sensor, also the
target measurement can be a solid,
a uid or a gas. Moreover, there is a
medium between the objective measu-
rement with the designed sensor. The
transduction effect can be given by
contact even though it cannot be to-
tally in contact among the transducer
with the target measurement, because
of for example, there are some parti-
cles or molecules of different compo-
sition of the target measurement with
the transducer, between the surface
of high temperature with a xed ther-
mocouple. Otherwise, for non-contact
measurement, it can be achieved the
measurement through the electromag-
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PAIDEIA XXI
netic wave that could get interaction
between the target measurement, the
medium and the transducer. Perhaps,
in future the quantum coherence in
sensors could make that the time de-
lays would be obtained between the
interaction of the target measurement
with the transducer could tend to be
instantaneous and could not depend
of the separation distance (García et
al., 2021). As a consequence, whether
the transducer can be dened by the
material, the geometry and structure
it can help for the optimal measure-
ment due to more appropriated ener-
gy transmission as an effect of the
transducer. The proposed sensors of
the described work in this article are
designed for contact measurement or
wireless measurement. Moreover, it
is described by Schrödinger equation
the relationship between the electrons
and atoms between the target measu-
rement with the transducer structu-
re. It is necessary to analyze that the
short response time and the robust-
ness in the physical variable measu-
rement over the target has correlation
with the material and geometry/struc-
ture of the transducer, but the tradi-
tional transducers have not this effect.
However, in this research is proposed
this task and importance for the mea-
surement of physical variables, hen-
ce, while it is achieved short response
time and robustness during the mea-
surement then it is possible to use the
short response time according to give
sophisticated function over the trans-
ducer, as for example, the articial in-
telligence as it is known by the smart
sensor (Wichmann, 1971; Calderón et
al., 2019, 2022).
In the gure 6, there are subgures
A, B, and C that are showed regarding
the nanostructures designed for the
transducer samples of the smart
sensor. The subgure A shows a sample
that is based on nanostructures of
ferric particles that were stored over
amorphous nanoholes of Anodic
Aluminum Oxide (AAO). The subgure
B shows blocks of structures that
were based on nanoparticles of
Calcium Carbonate. The subgure
C shows nanoparticles of sodium
that were stored over amorphous
nanoholes that were based on AAO.
For every nanostructure showed in
the subgures above, the overage size
is around 1000 nm, hence, it was
possible to show them by an optic
microscope (Lei & Cai, 2006).
There were made experiments ac-
cording to evaluate the performance of
the designed sensors, some applica-
tions were given for the measurement
of the combustion motor surface vibra-
tion, according to give this information
to the user and it could be possible to
get understanding of the behavior of
the motor, whether it needs reparation
either it could need an analysis of the
right selection of the used fuel for the
combustion.
Manufacturing of smart sensors
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Therefore, in the gure 7 it is
showed the expected vibration curve
in voltage equivalent (blue color
curve), over which it is showed the
experimental curve of the amplitude
vibration of the surface combustion
motor vibration in voltage equivalent
(red color curve) achieved by a piezo-
electric sensor, as well as it is showed
the archived measurements by the de-
signed smart sensor in voltage equiva-
lent (green color curve).
In the gure 7 there are two graphs
and the rst one shows the ampli-
tude vibration that was analyzed lines
above. Furthermore, the second graph
shows the error of the measurement,
in which the blue color curve is the
result of the comparison between the
theoretical curve with the data that
was given by the designed smart sen-
sor, the green color curve is the result
of the comparison between the theo-
retical curve with the curve data pro-
portionated by a piezoelectric sensor.
Figure 6. Designed nanostructures over samples that are based
on AAO amorphous nanoholes.V
Figure 7. Experimental data analysis for the designed smart sensor
Calderón-Chavarri et al.
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In fact, it was analyzed in this re-
search some proposals to elaborate
smart sensors, moreover, some sug-
gestions that the manufacturing sen-
sors can be made in the Andes moun-
tains of Peru, owing to keep correla-
tion with the mining production, but
always caring for the environment
conditions.
Furthermore, in this research there
were suggested some topics to get an
optimal design for the measurement
of the designed transducers, which
are supported by the short response
time and high robustness of the trans-
ducer samples that were based in
amorphous nanostructures, as well
as it was showed some applications of
the designed smart sensors over com-
bustion motors that are quite used in
public transport of Perú.
ACKNOWLEDGMENT
It is expressed deep warm
gratefulness to Aleksandra Ulianova de
Calderón due to her support according
to understand the importance of the
necessity that the engineering can be
quite important solution to connect
the researchers of the country with its
development technology, but always
with respect of the own ancestral
knowledge of all ethnic communities. It
is expressed special thankful to Carlos
Luis Calderón Soria, because of his
support during the experiments and
his permission to make experiments
with the Nissan Frontier 2003, in
which the performance of the designed
smart sensor was evaluated.
Author contributions: CRediT (Con-
tributor Roles Taxonomy)
JACCh = Jesús Alan Calderón-Cha-
varri
EBBG = Eliseo Benjamín Barriga-Ga-
marra
JCTF = Julio César Tafur-Sotelo
JHLJ = John Hugo Lozano-Jáuregui
HRLN = Hugo Rósulo Lozano-Núñez
AJQM = Álex John Quispe-Mescco
FACC = Freddy Alan Ccarita-Cruz
Conceptualization: JACCh
Data curation: JACCh
Formal Analysis: JACCh, EBBG,
JCTS, JHLJ, HRLN, AJQM, FACC
Funding acquisition: JACCh, EBBG,
JCTS, JHLJ, HRLN, AJQM, FACC
Investigation: JACCh, EBBG, JCTS,
JHLJ, HRLN, AJQM, FACC
Methodology: JACCh, JHLJ, HRLN,
AJQM
Project administration: JACCh, EBBG,
JCTS, JHLJ, HRLN
Resources: JACCh, EBBG, JCTS, JHLJ,
HRLN, AJQM, FACC
Software: JACCh
Supervision: JACCh, EBBG, JCTS, JHLJ,
HRLN, AJQM, FACC
Validation: JACCh, EBBG, JCTS,
JHLJ, HRLN, AJQM, FACC
Visualization: JACCh, EBBG, JCTS,
HRLN, AJQM, FACC
Writing – original draft: JACCh
Writing – review & editing: JACCh,
JHLJ, HRLN
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Received March 15, 2023.
Accepted May 11, 2023.
