JLMN-Journal of Laser Micro/Nanoengineering Vol. 19, No. 2, 2024

Using Laser in the Fabrication of Graphene for Gas Sensing: A Digital
Twin Approach
José Carlos Santos-Ceballos1, Foad Salehnia*1, Alfonso Romero1, and Xavier Vilanova1
1

Universitat Rovira i Virgili, Microsystems Nanotechnologies for Chemical Analysis (MINOS),
Tarragona, Spain*
Corresponding author’s e-mail: foad.salehnia@urv.cat

Laser-induced graphene (LIG) is a novel 3D porous material formed by laser treatment of specificcarbon substrates, boasting remarkable properties including high porosity, electrical conductivity, andmechanical flexibility. Understanding the relationship between laser parameters and resulting LIG
characteristics is fundamental, and often investigated through computational methods. Leveraging its
unique attributes, LIG finds application in sensors, particularly gas sensing, owing to its characteristics and facile fabrication process. This study employs a digital twin to predict LIG characteristics
and explore their correlation with nitrogen dioxide (NO2) gas sensing efficacy. LIG sensors with various morphologies (woolly fibers, porous formation, cellular network) and electrical resistances were
fabricated and assessed for response and recovery to NO2 exposure. Results demonstrate morphologydependent performance, with woolly fibers morphology showing optimal response and recovery at
low resistance values. Conversely, porous formations and cellular networks exhibit superior response
at high resistance values but complete recovery at lower values. These findings underscore the criticalrole of laser parameter optimization in tailoring LIG for enhanced gas sensing capabilities.
DOI: 10.2961/jlmn.2024.02.2005
Keywords: digital twin, laser-induced graphene, gas sensing, machine learning, sensors
1. Introduction
Laser-induced graphene (LIG) is a newly emerging 3D
porous material crafted through the precise application of alaser beam on certain carbon precursor materials (e.g., polymers, leaf, paper, wood, and food) [1–4]. This single-stepmethod has surfaced as a substitute for conventional approaches to obtaining graphene. It can be performed in ambient air without any solvents, wet chemical procedures,subsequent treatments, or other supporting processes making it exceedingly attractive for industrial use [5-6].
The unique properties of LIG, including high porosity,
mechanical flexibility, and outstanding electrical conductivity, position it as an ideal material for a wide range of applications. These range from energy storage devices as supercapacitors [7], batteries [8]) and triboelectric nanogenerators[9], to microfluidic systems [10], catalysis systems [11], andwater purification [12]. Additionally, LIG has been used insensor applications including biosensors [13], and physicalsensors [14-15]. Particularly noteworthy is its use in gas
sensing due to its sensitivity and selectivity [16–21].
The optimization of laser graphitization involves tailoring process parameters, like average laser power and scan
speed, to specific materials. These parameters enable the direct writing of 3D porous structures with graphene-like carbon. Avoiding nonoptimal laser settings is crucial to prevent
undesired effects such as material ablation or incompletegraphitization [1-22]. The intricate relationship between thelaser configuration parameters and the LIG characteristics
obtained has been extensively studied, and several researchers have used computational methods to understand this relationship [23-24].

Digital twins are becoming increasingly important, offering virtual representations of physical machines, complicated processes, and complex systems through computational models. In material science and chemical synthesis,
digital twins accelerate the development of new materials bydepicting their structure, processes, and performance evolution [25]. They enable predictions of material behavior under different conditions and optimize manufacturing processes to ensure materials meet the required specifications[26]. In the case of the LIG, laser parameters have been virtually tested to understand their relationship with the syntheses through simulations within the digital twin [27]. This hashelped to predict the characteristics of the material without
physically experimenting, saving time and resources.
This work used a digital twin previously developed byour research group to estimate the characteristics of the fabricated LIG layer (morphology, electrical resistance) [27].
This simulation, integrated with machine learning techniques, explored the interplay between LIG's characteristics
and its efficacy in nitrogen dioxide (NO2) gas sensing.
2. Materials and methods
2.1 Sensing device fabrication and gas-sensing measurements
To understand the influence of the characteristics of the
LIG layer for gas detection, the workflow depicted in the diagram in Fig. 1 has been followed. The digital twin was initially employed to predict LIG's characteristics by various
laser parameters (speed, frequency, and power).

JLMN-Journal of Laser Micro/Nanoengineering Vol. 19, No. 2, 2024

Fig. 1 Block diagram shows the workflow in this research.

The models used were obtained through machine learning algorithms, specifically utilizing artificial neural networks and Gaussian process regression. This approach generated samples with three distinct morphologies (woolly fibers (WF), cellular networks (CN), and porous formations(PF)) exposed by Abdul Hafez et al. [22]. Each type exhibited different electrical resistance values. Table 1 shows thedifferent combinations of laser parameters (speed, frequency,
and power) used and the results obtained. Subsequently, various gas sensors were fabricated. The LIG gas sensors were
fabricated using a commercial polyimide film (50 µm thickness) as the substrate. LIG was synthesized using a carbon
dioxide (CO2) laser system (SYNRARD 48-2) with a wavelength of 10.6 μm and a maximum power of 25 W. The gas
sensors feature a sensitive area comprising a straight line
LIG (6 mm long and a width equal to 116 µm), with the previously predicted characteristics.
Then, the gas-sensing performance of manufactured sensors was evaluated in the gas measurement system shown in
Fig. 2. The sensors were put in an airtight Teflon chamberwith a volume of 35 cm3 at room temperature. The detection
chamber was connected to a gas supply and mixing systemthat used a cylinder of 100 ppm NO2 and another cylinder ofpure, dry air as a carrier. Then, the resistance of the different
sensors was monitored using a multimeter (HP 34972A, Agilent), and resistance changes were recorded while 50 ppm
of NO2 was applied. The sensors were stabilized under dry
air for 120 minutes before exposure to NO2 species for 15
minutes. To work under more realistic experimental conditions, the total flow was adjusted at a low rate (100 mL/minute) using a set of mass-flow controllers (Bronkhorst HighTech B.V.) and electro valves. The sensor response is defined

as ΔR/R0 expressed in percentage, where ΔR corresponds to
the resistance changes recorded over gas exposure (15minutes), while R0 is given by the sensor's resistance in theair (or baseline); simultaneously evaluating sensitivity andresponse time. Also, sensor recovery (the percentage of resistance recovery to the baseline 60 minutes after gas exposure) was analyzed.
Table 1 Shows the laser parameters (power, speed, frequency)
used, the LIG characteristics (morphology, electrical resistance)
obtained and the gas sensing performance (response, recovery) of
each of the sensors.
FreR0
Power Speed
quency Morphology(kΩ)(%) (mm/s)
(kHz)

50
50
40
50
20
10
30
30
5
10
30
30

400
500
700
800
300
300
600
700
75
200
600
500

5
15
17.5
10
17.5
15
15
20
15
7.5
20
10

Fig. 2 Block diagram shows the gas measurement system.

WF
WF
WF
WF
PF
PF
PF
PF
CN
CN
CN
CN

15.1
16.5
26.1
35.7
11.9
20.2
25.4
29.7
10.9
20.2
28.1
32.7

∆R/R0 Recov(%) ery (%)

0.8
1.0
0.4
0.9
0.6
0.6
0.8
0.9
0.8
0.6
1.1
1.2

100.0
100.0
90.5
75.6
100.0
82.3
68.4
70.1
100.0
85.9
61.6
78.1

JLMN-Journal of Laser Micro/Nanoengineering Vol. 19, No. 2, 2024

2.2 Characterization of the gas-sensitive materials
A Scios 2 DualBeam field emission scanning electron
microscope (FESEM) was used to analyze the morphologyof the synthesized layers. The Raman analysis was conducted using a Raman spectrometer (Renishaw plc) with acoupled confocal Leica DM2500 microscope (Leica Microsystems GmbH) with a laser of 514 nm wavelength.
3. Results and discussion
3.1 Structural and morphological studies of LIG
The FESEM images (Fig 3) from the fabricated LIG gas
sensor samples depict the three morphologies cited previously. Fig. 3a shows the initial stages of carbonization (porous formation), marked by the formation of porosity,wherein the polyimide precursor undergoes a transformative
process, yielding graphene-like structures. The formation ofinterconnected cellular networks of 3D graphene is shownin Fig. 3b. The presence of this morphology underscores theunique structural properties of the fabricated material, which
are distinct from conventional two-dimensional graphene
structures. In contrast, a remarkable transition to a fibrousmorphology characterized by the presence of woolly fibers
was observed under conditions of high laser fluxes. This observation suggests that the laser flux plays a pivotal role indictating the final morphology of the fabricated material, influencing the spatial arrangement and structural characteristics of the LIG.

Raman spectroscopy was performed along the LIG layers to investigate the correlation between the morphologies
and molecular structure of LIG. In the Raman spectrum(Fig.4) corresponding to the PF morphology, the appearanceof G and D peaks is observed, located in the typical positions(around 1580 cm-1 and 1350 cm-1) of the graphite formationin the material [6-22-23-28]. Furthermore, peaks G and Dshow a large full width at half maximum (FWHM), which
covers a wide band around the 2D peak, indicating the presence of porous graphite structures. The CN morphology exhibits a distinctive spectral signature characterized by a
sharper and more well-defined 2D band, accompanied by anotable reduction in the FWHM of both the G and D peaks.
The reduction in FWHM of the G peak suggests the formation of larger sp2 grains within the material, indicating thedevelopment of 3D graphene structure. The WF morphology
showed similar Raman spectra to those of the PF, characterized by the absence of a sharp 2D peak and a larger FWHM
for the G peak compared to the cellular networks. These results suggested that woolly fibrous structures possess lowerquality sp2 carbon than network cellular structures.

Fig. 4 Raman spectra of porous formations (red line), cellular networks (green line) and woolly fibers (blue line)
morphologies.

Fig. 3 Field emission scanning electron microscope(FESEM) images of a) porous formations, b) cellular
networks and c) woolly fibers morphologies.

3.2 Gas sensing performance of LIG
The sensing performance of the LIG gas sensors developed for NO2 detection was assessed under conditions likethose required for real-time monitoring. Fig. 5 illustrates theelectrical response observed for one of the LIG gas sensors
fabricated, which exhibited decreased resistance upon exposure to the target gas (50 ppm of NO2). The reduced resistance results from the increased hole density and electrical conductivity of the p-type LIG upon exposure to the oxidating NO2 species. Additionally, it is observed that the sensor can increase its electrical resistance until it recovers thebaseline when it stops being exposed to NO2. The longer recovery time (60 minutes) compared to the response time (15minutes), is attributed to the persistence of gas molecules
within the sensitive layer, requiring a longer duration for

JLMN-Journal of Laser Micro/Nanoengineering Vol. 19, No. 2, 2024

desorption or complete elimination during re-exposure to air.
Considering that this experiment has been carried out atroom temperature, the use of a heater or ultraviolet lightwould facilitate the desorption of the gas, thus reducing therecovery time [29–31].

Fig. 5 Electrical response of a LIG gas sensor under 50
ppm of NO2.

Fig. 6(a and b) reveals that, in the case of the first phaseof graphitization (PF morphology), the response of the sensors is better at high electrical resistance baseline values (R0).
However, at low values, the sensor recovery is complete(100% in less than 50 minutes). This performance pattern isalso observed in samples with CN morphology (Fig. 6(c andd)). The WF morphology (Fig. 6(e and f)) exhibits improved
response and recovery at low electrical resistance values.
Conclusively, an intelligent selection of the laser's parameters in the fabrication of LIG is crucial for optimizing theirgas sensing capabilities. The study establishes that LIG witha woolly fibers morphology and low electrical resistance exhibits the most promising characteristics for effective gas
sensing applications.

Fig.6 Sensors response (left side) and sensor recovery (leftside) corresponding to LIG gas sensors with different morphologies and baseline electrical resistance.

4. Conclusions
In conclusion, laser-induced graphene (LIG) stands out
as a versatile material with exceptional properties for gas
sensing applications. Through meticulous control of laserparameters, LIG can be precisely tailored to exhibit specificmorphologies and electrical resistances, influencing its

performance in detecting gases such as NO2. Our study, integrating digital twin simulations and machine learning techniques, highlights the importance of this parameter optimization process. We observed that LIG with woolly fibers
morphology and low electrical resistance shows the mostpromising characteristics for effective gas sensing. However,
we also noted that different morphologies exhibit distinct response and recovery patterns, indicating a nuanced relationship between morphology, resistance, and sensing performance. These findings underscore the significance of intelligent material design in maximizing gas sensing capabilities.
Moving forward, further exploration into the underlying
mechanisms governing the relationship between LIG characteristics and gas sensing performance will be crucial foradvancing the development of highly efficient and selective
gas sensing technologies. Considering the results of thiswork, our next investigations will be directed in two main
directions. The first involves using the same methodology to
explore the relationship between the characteristics of the
LIG in different sensing areas with digital twins and the second is improving the gas sensing performance of the LIG by
doping and compositing it with other materials, such as
metal oxides or metals for selective gas sensors.
Acknowledgments
The research has been funded by the EU in the framework of Marie Sklodowska-Curie Actions, grant 823895PE; MICINN, grants PDC2022-133967-I00 and
TED2021131442B-C31, as well as AGAUR grant 2021
SGR 00147, J.C.S-C. Thanks to the predoctoral program
AGAUR-FI ajuts (2023 FI-2 00180) Joan Oró of the Secretariat of Universities and Research of the Department of Research and Universities of the Generalitat of Catalonia andthe European Social Plus Fund, F.S. thanks the Spanish Ministry of Universities, Recovery, Transformation, and Resilience Plan, and the Maria Zambrano grant Funded by the European Union – NextGenerationEU (grant number
2021URV-MZ-13).
References[1] T. D. Le, H. Phan, S. Kwon, S. Park, Y. Jung, J. Min, B.
J. Chun, H. Yoon, S. H. Ko, S. Kim, and Y. Kim: Adv.
Funct. Mater., 32, (2022) 2205158.
[2] R. Ye, D. K. James, and J. M. Tour: Adv. Mater., 31,(2019) 1803621.
[3] Y. Chyan, R. Ye, Y. Li, S. P. Singh, C. J. Arnusch, andJ. M. Tour: ACS Nano., 12, (2018) 2176.
[4] J. Lin, Z. Peng, Y. Liu, F. Ruiz-Zepeda, R. Ye, E. L. G.
Samuel, M. J. Yacaman, B. I. Yakobson, and J. M.
Tour: Nat. Commun., 5, (2014) 5714.
[5] K. Avinash, and F. Patolsky: Mater. Today., 70, (2023)
104.
[6] R. Ye, D. K. James, and J. M. Tour: Acc. Chem. Res.,
51, (2018) 1609.
[7] W. Ma, J. Zhu, Z. Wang, W. Song, and G. Cao: Mater.
Today Energy., 18, (2020) 100569.
[8] F. Zhang, E. Alhajji, Y. Lei, N. Kurra, and H. N. Alshareef: Adv. Energy Mater., 8, (2018) 1800353.
[9] K.-H. Choi, S. Park, S.-K. Hyeong, S. Bae, J.-M. Hong,
T.-W. Kim, S. H. Lee, S. Ryu, and S.-K. Lee: J. Mater.
Chem. A, 8, (2020) 19822.

JLMN-Journal of Laser Micro/Nanoengineering Vol. 19, No. 2, 2024

[10] J. Zhu, X. Huang, and W. Song: ACS Nano., 15, (2021)
18708.
[11] X. Han, R. Ye, Y. Chyan, T. Wang, C. Zhang, L. Shi, T.
Zhang, Y. Zhao, and J. M. Tour: ACS Appl. Nano Mater., 1, (2018) 5053.
[12] N. H. Barbhuiya, A. Kumar, and S. P. Singh: Trans. Indian Natl. Acad. Eng., 6, (2021) 159.
[13] Z. Wan, N.-T. Nguyen, Y. Gao, and Q. Li: Sustainable
Mater.Technol., 25, (2020) e00205.
[14] A. Kaidarova, and J. Kosel: IEEE Sens. J., 21, (2021)
12426.
[15] S. Movaghgharnezhad, and P. Kang: J. Mater. Chem. C.,
12, (2024) 6718.
[16] M. G. Stanford, K. Yang, Y. Chyan, C. Kittrell, and J.
M. Tour: ACS Nano., 13, (2019) 3474.
[17] J. C. Santos-Ceballos, F. Salehnia, A. Romero, X.
Vilanova, and E. Llobet: IEEE Sens. J., 24, (2024) 9366.
[18] S.-F. Tseng, P.-S. Chen, S.-H. Hsu, W.-T. Hsiao, and
W.-J. Peng: Appl. Surf. Sci., 620, (2023) 156847.
[19] Z. Peng, L.-Q. Tao, S. Zou, C. Zhu, G. Wang, H. Sun,and T.-L. Ren: Chem. Eng. J., 428, (2022) 131079.
[20] L. Yang, G. Zheng, Y. Cao, C. Meng, Y. Li, H. Ji, X.
Chen, G. Niu, J. Yan, Y. Xue, and H. Cheng: Microsyst.
Nanoeng., 8, (2022) 78.
[21] L. Yang, N. Yi, J. Zhu, Z. Cheng, X. Yin, X. Zhang, H.
Zhu, and H. Cheng: J. Mater. Chem. A., 8, (2020) 6487.
[22] M. Abdulhafez, G. N. Tomaraei, and M. Bedewy: ACS
Appl. Nano Mater., 4, (2021) 2973.
[23] R. Murray, M. Burke, D. Iacopino, and A. J. Quinn:
ACS Omega., 6, (2021) 16736.
[24] H. Wahab, V. Jain, A. S. Tyrrell, M. A. Seas, L.
Kotthoff, and P. A. Johnson: Carbon., 167, (2020) 609.
[25] S. R. Kalidindi, M. Buzzy, B. L. Boyce, and R. Dingreville: Front. Mater., 9, (2022) 818535.
[26] H. Priyadarshi, G. Ahmed, D. Mishra, A. K. Srivastava,
R. McGee, A. Shrivastava, and K. Singh: Crit. Rev.
Solid State Mater. Sci., 1, (2024) 1.
[27] J. C. Santos-Ceballos, F. Salehnia, A. Romero, and X.
Vilanova: Sci. Rep., 14, (2024) 10363.
[28] L. X. Duy, Z. Peng, Y. Li, J. Zhang, Y. Ji, and J. M.
Tour: Carbon., 126, (2018) 472.
[29] Y. Zhang, S. Li, J. Zhang, Z. Pan, D. Min, X. Li, X.
Song, and J. Liu: Sci. Rep, 3, (2013) 1[30] Á. Peña, D. Matatagui, F. Ricciardella, L. Sacco, S.
Vollebregt, D. Otero, J. López-Sánchez, P. Marín, and
M. C. Horrillo: Appl. Surf. Sci., 610, (2023) 155393.
[31] D. H. Lee, and H. Yoo: Sensors 2022, 22, (2022) 9228.

(Received: June 4, 2024, Accepted: August 18, 2024)