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Strain-Tuned 2D Materials with Sharper Detection of Toxic Gases

Researchers have shown that carefully engineered two-dimensional materials can significantly improve the detection of hazardous gases, using mechanical strain as a tuning knob.

gas lit up by blue and green light swirls under a spotlight. Study: Strain-Tunable Gas Sensing Properties of Ag- and Au-Doped SnSe2 Monolayers for the Detection of NO, NO2, SO2, H2S and HCN. Image Credit: faak/Shutterstock.com

In a study published in Nanomaterials, scientists report that silver- and gold-doped tin diselenide (SnSe2) monolayers exhibit highly selective, strain-tunable gas sensing behavior toward nitrogen monoxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), hydrogen sulfide (H2S), and hydrogen cyanide (HCN).

Using first-principles simulations, the team demonstrated how noble-metal doping and biaxial strain together control adsorption strength, electronic response, and recovery behavior - key factors that determine whether a gas sensor is both sensitive and practical.

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Detecting toxic gases at low concentrations remains a challenge for conventional sensors, which often struggle with limited sensitivity or slow recovery.

Two-dimensional materials such as transition-metal dichalcogenides have attracted growing attention because their atomically thin structure exposes a large surface area and allows their electronic properties to be tuned.

SnSe2, a layered semiconductor, is particularly attractive due to its high carrier mobility and surface reactivity.

The study shows that substituting a single selenium atom with a noble metal atom (silver or gold) fundamentally alters SnSe2’s electronic structure, increasing conductivity and strengthening interactions with gas molecules.

These changes create a responsive device whose sensing behavior can be further adjusted using mechanical strain.

Conducting the Study

The researchers employed density functional theory calculations, implemented in CASTEP, using the GGA-PBE exchange-correlation functional.

A 3 × 3 × 1 SnSe2 supercell was fabricated, replacing one selenium atom with gold or silver, resulting in a doping concentration of approximately 3.7 %.

Five toxic gases, NO, NO2, SO2, H2S, and HCN, were adsorbed onto the doped monolayers, and their adsorption energies, charge transfer, equilibrium distances, and electronic structure changes were analyzed.

To explore tunability, biaxial strain ranging from −8 % (compressive) to +6 % (tensile) was applied, allowing the team to examine how mechanical deformation modifies gas–surface interactions and recovery behavior.

NO2 Emerges as the Most Sensitive Target

Among all gases studied, NO2 stood out. It exhibited the strongest interaction with both Ag- and Au-doped SnSe2, with adsorption energies of −1.03 eV and −1.12 eV, respectively. Substantial charge transfer from the substrate to the molecule further confirmed strong chemisorption, directly influencing the electrical response of the sensor.

Interestingly, NO2 adsorption also drives a transition from metallic to semiconducting behavior in the doped SnSe2 systems. Changes in the density of states near the Fermi level amplified resistance modulation, reinforcing NO2 selectivity and making it the most detectable gas in the study.

Other gases interacted more weakly. H2S and NO show intermediate adsorption strengths, while SO2 and HCN are dominated by physisorption, with smaller charge transfer and weaker electronic perturbation.

Strain Engineering Enables Gas-Specific Control

A central finding was that strain does not affect all gases equally. Instead, different gases respond preferentially to either compressive or tensile deformation.

Compressive strain enhanced adsorption for NO, NO2, and SO2, particularly on Ag-doped SnSe2. For example, the adsorption energy of NO2 on Ag–SnSe2 increased to −1.33 eV under −8 % strain, further boosting sensitivity.

In contrast, H2S and HCN on Au-doped SnSe2 responded more strongly to tensile strain, highlighting the gas- and dopant-specific nature of strain tuning. This selectivity means strain engineering can be used not simply to increase sensitivity, but to tailor sensor response toward specific target gases.

High sensitivity alone is not enough for real-world sensors; gas molecules must also desorb quickly so the sensor can be reused.

The study found that NO2, despite its strong detectability, had the slowest recovery at room temperature due to its strong chemisorption and pronounced electronic coupling.

However, simulations showed that increasing the operating temperature or applying appropriate strain can dramatically shorten recovery times, bringing NO2 desorption into a practical range.

Other gases, including SO2, NO, and HCN, demonstrated faster recovery under most conditions, making them easier to cycle repeatedly.

Implications for Environmental Monitoring and Safety

The results suggest that Ag- and Au-doped SnSe2 monolayers could serve as highly tunable platforms for next-generation gas sensors. Their strong NO2 selectivity, combined with strain- and temperature-assisted recovery, makes them particularly promising for air-quality monitoring, industrial safety, and public health applications.

The authors suggest that further improvements could be achieved by exploring alternative dopants, combining multiple two-dimensional materials, or optimizing strain conditions for specific sensing environments.

While the study is theoretical, it provides clear guidance for experimental efforts aimed at building strain-tunable gas sensors based on two-dimensional materials.

As concerns over air pollution and toxic gas exposure continue to grow, such adaptable sensing platforms may play an increasingly important role in real-time environmental monitoring and safety systems.

Journal Reference

Ma, Y., et al. (2025, December). Strain-Tunable Gas Sensing Properties of Ag- and Au-Doped SnSe2 Monolayers for the Detection of NO, NO2, SO2, H2S and HCN. Nanomaterials, 15(18), 1454. DOI: 10.3390/nano15181454 

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Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.

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