Dr. Matthew R. Dent, an assistant professor of chemistry in Wayne State’s College of Liberal Arts and Sciences, has received a Faculty Early Career Development (CAREER) award from the U.S. National Science Foundation, one of the agency’s most competitive honors for early-career researchers. The five-year, $749,702 grant supports a project titled “CAREER: Carbon Monoxide Sensing, Selectivity, and Signaling in Microbes.” The award is designed to integrate research with education and public engagement, and Dent’s plan does exactly that, pairing laboratory investigation of gas-sensing proteins with training for students and hands-on outreach to the Detroit community.

The scientific premise of the project rests on a striking paradox. Carbon monoxide is not merely an industrial pollutant or a household hazard; it is produced naturally inside nearly all living organisms, including humans. In mammalian biology, it is generated during the breakdown of heme by heme oxygenase enzymes and plays documented roles in regulating cellular growth, cell death, and inflammation. Researchers have long suspected that this endogenous gas could be harnessed for novel human therapeutics, perhaps as a modulator of immune responses or a protector of transplanted tissue. At the same time, certain bacteria can literally “eat” carbon monoxide, oxidizing it and channeling the electrons into their metabolism as an alternative fuel when more conventional nutrients are scarce. That dual identity, therapeutic agent and microbial food, makes carbon monoxide one of the most intriguing small molecules in biology.

What makes the situation scientifically urgent is how little is actually known about the detection side of the equation. Despite decades of work on carbon monoxide’s chemistry and toxicity, researchers still have a surprisingly incomplete picture of how biological systems sense the gas and convert that detection into concrete cellular actions. How does a bacterium know carbon monoxide is present? How does it distinguish carbon monoxide from the chemically similar gases that surround it, such as oxygen and nitric oxide? And how does a molecular encounter with a diatomic gas translate into changes in gene expression that reshape the cell’s behavior? These are the questions Dent’s laboratory is built to answer.

The experimental strategy centers on microbial transcription factors, the proteins that control gene expression by binding to DNA and switching genes on or off. In carbon monoxide-responsive transcription factors, the gas is recognized through specialized metal ion sites, most commonly an iron atom held within the protein in a heme-like coordination environment. When carbon monoxide binds to that metal center, it changes the geometry and electronic state of the complex, and that change propagates through the protein’s structure to alter its interaction with DNA. The result is a direct molecular line of communication between the gas in the environment and the genome of the cell. Because these proteins bind carbon monoxide tightly and selectively, they serve as powerful and experimentally accessible models for studying gas signaling in general.

Dent’s team will pursue this problem by integrating several complementary techniques. Protein biochemistry will reveal how the sensor proteins behave in isolation and how their DNA-binding properties shift when carbon monoxide is present. Cell biology experiments will connect those molecular events to observable changes in living microorganisms. Genomic approaches will allow the researchers to scan across diverse microbial species and identify previously unknown carbon monoxide-sensing proteins, expanding the known roster of these molecular detectors far beyond the handful of well-characterized examples. Finally, bioinorganic spectroscopy will probe the metal centers themselves, determining the structural blueprints that allow a protein to grab carbon monoxide while ignoring the chemically related gases that could otherwise trigger false signals. Together, these approaches promise a comprehensive picture of gas sensing at the atomic level.

The potential applications extend well beyond basic microbiology. If researchers can define the rules that govern selective gas recognition in proteins, those rules could inform the design of new biosensors capable of detecting carbon monoxide or other gases with high precision, with uses ranging from environmental monitoring to medical diagnostics. The same knowledge could guide the development of carbon monoxide-releasing molecules as therapeutic agents, where controlled delivery of the gas could exploit its anti-inflammatory and cytoprotective effects. And because some microbes can grow on carbon monoxide as a fuel, a deeper understanding of the underlying biology could contribute to alternative bioenergy strategies that convert waste gases into useful products. “Carbon monoxide has a notorious reputation as a poison, but in biology, it is also a powerful signal and an energy currency for certain microbes,” Dent said in the university’s announcement. “By studying how microbes selectively identify CO at the molecular level, we can learn how living cells harness this gas. These fundamental discoveries could open exciting doors for designing new medical treatments, developing biosensors and advancing alternative bioenergy strategies.”

The education and outreach component of the CAREER award is woven into the research program rather than bolted on. Dent’s project will train both undergraduate and graduate students in STEM fields, giving them experience not only in laboratory research but also in science communication, a skill increasingly recognized as essential for scientists working on topics, like gas toxicity and blood chemistry, that intersect directly with public health. The outreach arm of the project takes the form of a hands-on community program exploring the biochemistry of blood, connecting the molecular story of carbon monoxide, hemoglobin, and oxygen transport to concepts people encounter in everyday life. The program is designed to make the invisible chemistry of the bloodstream tangible and to draw community members into a conversation about science that affects their health.

That outreach effort has already been tested in the field. In a partnership with Wayne State University Athletics, Dent and his team piloted the program earlier this year at the Fourth Annual I-96 Blood Battle, a blood drive competition between Wayne State and Davenport University. The event proved a fitting venue for a project rooted in the chemistry of blood, and it delivered a decisive result for the home team: the Wayne State Warriors secured twice as many blood donations as the Davenport Panthers, winning the competition soundly. Dent has indicated that he looks forward to continuing the community partnership in the coming years, using the blood drive as a recurring platform for the outreach program.

Leadership at Wayne State has framed the award as emblematic of the university’s broader research ambitions. “Dr. Dent’s groundbreaking investigation is an excellent example of the caliber of pioneering research taking place across Wayne State University’s campus,” said Dr. Ezemenari M. Obasi, vice president for research and innovation. “By bridging chemistry with future applications in human health, environmental sustainability and community-engaged STEM education, this project reflects our commitment to translating research that will impact our community and beyond.” The CAREER program, which funds the project under grant number 2540612, specifically recognizes early-career faculty with the potential to serve as academic role models and to lead advances in their institutions’ missions. For a molecule long defined by its dangers, the award marks a shift in perspective: carbon monoxide as a key to understanding how life senses, signals, and survives, investigated from a laboratory in Detroit with results that could ripple outward into medicine, energy, and the education of the next generation of scientists.

Subject of Research: Microbial carbon monoxide sensing, selectivity, and signaling by gas-binding transcription factor proteins

Article Title: From silent killer to cellular fuel: Wayne State chemist unlocks nature’s carbon monoxide sensors

Article References: From silent killer to cellular fuel: Wayne State chemist unlocks nature’s carbon monoxide sensors. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: carbon monoxide, NSF CAREER award, Wayne State University, microbiology, transcription factors, gas sensing, bioinorganic chemistry, heme proteins, biosensors, bioenergy, science outreach, gene expression

The work, carried out by Abhishek Kumar Pandey, Deepak Jain, and Catherine Baskiotis, exploits spontaneous four-wave mixing, a nonlinear optical process in which two pump photons at the same frequency are annihilated to create a correlated pair of photons at two very different frequencies. Energy conservation forces the sum of the two new frequencies to equal twice the pump frequency, while momentum conservation, known as the phase-matching condition, dictates which frequency combinations can actually occur. In a waveguide, the phase-matching condition can be engineered by shaping the geometry of the silicon core, allowing the generated signal and idler photons to be tuned with remarkable precision across a wide spectral range.

What makes the new designs remarkable is the sheer distance between the two photons. The flagship design, targeted at atmospheric quantum key distribution, produces a signal photon at 3.905 micrometres, deep inside a mid-infrared atmospheric transparency window, while its idler partner lands at 1.541 micrometres in the telecom C-band. That separation of roughly 2,364 nanometres is dramatically larger than the record of about 1,125 nanometres theoretically reported in earlier all-solid silicon waveguide studies. Crucially, the design achieves this in the true quantum regime, where at most one photon pair is produced per pump pulse, rather than relying on the high pump powers used in classical demonstrations.

This distinction matters. Previous experiments with air-clad silicon waveguides demonstrated large signal-idler separations of about 2,058 nanometres, but only by using pump peak powers around 20 watts to achieve phase matching. In the quantum regime, pump power must be kept low to suppress multi-pair emission, and with those earlier designs the phase-matched separation collapsed to 1,780 nanometres, placing the mid-infrared photon outside any atmospheric transmission window. The new all-solid designs sidestep this problem by building the phase matching into the waveguide geometry itself, so that it holds even at the milliwatt pump levels required for single-pair emission.

The choice of an all-solid, fully oxide-clad structure is itself strategic. Air-clad waveguides suffer from significant scattering losses caused by sidewall roughness, because the optical mode interacts strongly with the etched surfaces. By surrounding the silicon core with fused silica cladding and a buried oxide layer, the proposed waveguides reduce the modal overlap with etched sidewalls, mitigating fabrication-induced scattering. The team’s calculations show that even at the longest signal wavelength, where silica is nominally opaque, the majority of the optical power remains confined to the silicon core, keeping total material attenuation over the 2-centimetre waveguide to acceptable levels.

To give their predictions quantitative teeth, the researchers adopted a model for the probability of photon-pair generation per pulse and validated it against published experimental data from photon-pair generation in photonic crystal fibre. By modelling the fibre as a simple step-index structure and feeding in the experimental parameters, they computed photon-pair rates that closely matched the measured values across a range of pump powers, outperforming the original theoretical treatment of those experiments. Because the model takes as inputs only the modal properties, the nonlinear index, and the operating conditions, the authors argue that the validation is generic and applies equally to the silicon waveguide geometries, whose modal fields they computed independently using full-vectorial finite element simulations.

Each of the three proposed waveguides is tailored to a specific application. Two designs generate signal photons at 3.265 and 3.461 micrometres, which coincide with absorption bands of methane and nitrogen dioxide respectively, the fingerprint region where these gases reveal their presence. Paired with telecom-band idlers, these sources could power schemes for sensing with undetected light, in which the mid-infrared photon probes the gas while only the telecom photon is ever detected, allowing the entire measurement to be performed with cheap, mature silicon-compatible detectors. The third design, optimised for atmospheric quantum key distribution, places the signal photon at 3.905 micrometres, where atmospheric transmission is dramatically better than at the 1.55-micrometre wavelength used in current daylight quantum communication experiments.

The shift to the mid-infrared could be transformative for free-space quantum communication. At 3 to 4 micrometres, the atmosphere exhibits reduced Rayleigh scattering, lower solar background noise, and greater resistance to weather-related variations, all of which translate into more reliable quantum links and an improved ability to operate in daylight. Shrinking the photon-pair source onto a chip also addresses a critical constraint for satellite payloads, where every gram of mass and cubic centimetre of volume counts. Silicon-on-insulator fabrication is cheap, scalable, and compatible with existing CMOS foundries, making the prospect of flight-qualified quantum sources considerably more realistic than with bulk nonlinear crystals.

The team also addressed the practical obstacles that have plagued mid-infrared quantum optics in silicon. Spontaneous Raman scattering, which can contaminate photon-pair spectra, is predicted to be negligible because the generated photons lie more than 330 Raman linewidths away from the silicon Raman gain peak, placing the Raman gain at the signal frequencies at the level of one part in a million. Two-photon absorption, the bane of silicon nonlinear optics at telecom wavelengths, falls off sharply for pump wavelengths above 1.9 micrometres, and the chosen pump wavelengths between 2.1 and 2.21 micrometres sit in a sweet spot combining strong Kerr nonlinearity with suppressed carrier generation. Numerical tolerance studies further showed that small fabrication imperfections of plus or minus 10 nanometres in the core dimensions cause only slight wavelength drifts that can be compensated by tuning the pump laser.

The predicted operating points are within reach of existing technology. Each design targets a probability of photon-pair generation per pulse of about 0.05, a value considered a practical compromise between source brightness and multi-pair suppression, and achieves this with peak pump powers between 9.2 and 32.2 milliwatts from 5-picosecond pulses. Such pump powers are already delivered by mature thulium and holmium-doped solid-state lasers around 2 micrometres, while integrated 2-micrometre laser sources continue to advance toward the required output levels. With mid-infrared single-photon detectors, including superconducting nanowire devices and frequency-upconversion schemes, progressing rapidly, the authors argue that their chip-scale sources could soon move from simulation to experiment, opening a path toward quantum communication and sensing that spans from the fingerprint region of the molecules to the heart of the telecom infrastructure.

Subject of Research: Silicon waveguide design for quantum-correlated photon-pair generation bridging mid-infrared and telecom bands via spontaneous four-wave mixing

Article Title: Quantitative study of silicon waveguides for the generation of quantum correlated photon pairs bridging mid-infrared and telecom bands

Article References: Pandey, A. K., Jain, D., & Baskiotis, C. (2026). Quantitative study of silicon waveguides for the generation of quantum correlated photon pairs bridging mid-infrared and telecom bands. Results in Optics, 25, Article 101158. https://doi.org/10.1016/j.rio.2026.101158

Image Credits: AI Generated

DOI: 10.1016/j.rio.2026.101158

Keywords: silicon photonics, quantum optics, photon pairs, four-wave mixing, mid-infrared, telecom C-band, quantum key distribution, gas sensing, silicon-on-insulator, phase matching, Raman scattering, integrated photonics

A new study published in the journal Artificial Intelligence & Environment addresses this dual challenge with a multitask deep learning framework that simultaneously predicts how strongly a candidate material adsorbs sulfur gases and how effectively it senses them. The work was carried out on transition-metal phthalocyanines, a versatile family of planar organic molecules whose electronic behavior can be systematically tuned by swapping the metal atom anchored at the center of the macrocycle. By coupling artificial intelligence with density functional theory calculations, the team built a computational pipeline capable of screening gas-material combinations far more rapidly than would be possible through quantum-chemical simulation alone, offering a faster route to multifunctional materials for environmental monitoring and purification.

The researchers constructed their dataset by examining 28 transition-metal phthalocyanine materials against four sulfur-based gases: hydrogen sulfide (H₂S), sulfur monoxide (SO), sulfur dioxide (SO₂) and sulfur trioxide (SO₃). This systematic combination produced 78 distinct gas-material adsorption systems, each characterized through DFT calculations that yield two key quantities. The first is adsorption energy, a thermodynamic measure of how strongly a gas molecule clings to the material surface. The second is sensing response, which captures how much the interaction alters the electronic or conductive properties of the material, and therefore how visible the gas would be to an actual sensor device built from it.

One of the central insights motivating the study is that gas sensing and gas adsorption are closely connected but not governed by exactly the same underlying physics. A material that binds a gas too weakly may fail to capture it; one that binds too strongly may trap the molecule irreversibly, poisoning the sensor and preventing recovery. According to Xiliang Yan, a corresponding author of the study, the goal was to build a model that could learn what the two processes have in common while preserving the information unique to each. Multitask learning is well suited to this kind of problem because it forces the network to internalize shared physicochemical structure across related prediction problems, which often improves generalization when training data are limited.

Architecturally, the framework departs from conventional machine-learning workflows that predict one material property at a time. Instead of training isolated single-task regressors or classifiers, the multitask model learns a common internal representation of the physicochemical features and then routes that representation into separate prediction branches, one dedicated to adsorption energy and one to sensing response. This design allows the model to explicitly evaluate the trade-off at the heart of multifunctional material design: the balance between how tightly a gas binds to a material and how strongly that binding event perturbs the material’s electrical behavior. Candidates can thus be ranked not only for raw performance on either metric but for the practically important combination of both.

The performance gains on an independent test set were substantial. For the classification task, distinguishing materials that produce high versus low sensing responses, the multitask model achieved an accuracy and F1 score of 0.83 along with a recall of 0.88. By comparison, several conventional single-task machine-learning models tested on the same problem reached accuracies of only 0.44 to 0.63, a margin that underscores how much information the shared representation recovers from a small dataset. For the regression task of predicting adsorption energies, the model attained a test-set R² of 0.86, with a root mean square error of 0.46 electron volts and a mean absolute error of 0.35 electron volts. Those error bars are meaningful in the context of DFT-based screening, where candidate ranking depends on relative rather than absolute precision.

Beyond aggregate metrics, the framework pinpointed specific candidate materials with distinct functional profiles. Iron-centered phthalocyanine, abbreviated Fe/Pc, emerged as a strong sensitivity candidate for sulfur monoxide, and Fe/Pc together with chromium-centered Cr/Pc also displayed promising responses to hydrogen sulfide. Other members of the transition-metal phthalocyanine family exhibited particularly strong adsorption of SO and SO₂, suggesting a complementary role as gas-removal materials rather than sensors. Because the metal center is synthetically tunable, these computational assignments translate directly into design guidance: choosing the central atom steers the material toward detection, capture, or a useful blend of the two.

Importantly, the team designed the model to be interpretable rather than treating it as an opaque prediction engine. Feature analysis revealed that the atomic radius and electronic properties of the transition-metal center, the electronic structure of the surrounding phthalocyanine framework, and the intrinsic properties of the gas molecule all contribute measurably to both sensing and adsorption behavior. Yan noted that the model does more than identify promising candidates; it also helps reveal which physical and electronic characteristics control adsorption and sensing, providing guidance for the rational design of new materials. In practice, this means each prediction comes with an explanation, allowing chemists to reason about why a given metal center performs well and to extrapolate those lessons to materials outside the original training set.

The authors are candid about the limitations of the current work. The model was trained on a relatively small dataset, an unavoidable constraint given the computational cost of generating high-quality DFT labels for dozens of gas-material systems. Expanding the framework to additional metals, a broader catalog of gases and more diverse adsorption configurations could substantially improve its ability to screen previously unexplored materials and reduce uncertainty at the edges of chemical space. Still, the study demonstrates a general template: interpretable multitask learning can connect adsorption thermodynamics with gas-sensing behavior in a single computational strategy, pointing toward a new generation of multifunctional materials that both detect and remove hazardous gases before they reach lungs, factories or the atmosphere.

Subject of Research: DFT-driven multitask deep learning for predicting sulfur gas sensing and adsorption by transition-metal phthalocyanines

Article Title: Deep learning helps scientists design materials that can both detect and capture toxic sulfur gases

Article References: Deep learning helps scientists design materials that can both detect and capture toxic sulfur gases. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: deep learning, gas sensing, hydrogen sulfide, sulfur dioxide, phthalocyanines, density functional theory, adsorption energy, multitask learning, environmental monitoring, gas capture, interpretable AI, materials discovery

Gas sensors are central to that task, converting gas concentration into electrical responses through changes in conductivity and charge carrier behavior. Among many materials, zinc oxide (ZnO) stands out for its responsiveness, reasonable selectivity, and relatively fast recovery after exposure. Yet ZnO’s performance under realistic industrial conditions often falls short, limiting sensitivity, stability, and overall detection reliability.

A research team addressed this challenge by engineering ZnO nanostructures and enhancing them with noble metals. They first synthesized ZnO nanoflakes using a hydrothermal method, chosen for its operational simplicity, strong crystallinity, and tunable morphology without templates or surfactants. This step created a surface architecture designed to support efficient gas adsorption.

Next, they loaded gold (Au), platinum (Pt), and palladium (Pd) nanoparticles onto the ZnO nanoflakes using an ultraviolet (UV) reduction approach. Compared with conventional chemical reduction or impregnation, UV-assisted reduction proceeds under mild conditions, reducing the need for extra reducing agents. That helps limit unwanted surface contamination and supports the formation of small, uniformly distributed metal nanoparticles.

The resulting noble metal/ZnO heterostructures benefit from intimate interfacial contact, a key factor for gas sensing. Such junctions can influence charge transfer pathways, alter surface chemisorption states, and improve the responsiveness of the semiconductor during VOC exposure. In testing, the Au-modified ZnO delivered stronger response signals to isopropanol than the unmodified nanoflakes.

The study also explored sensing behavior toward hydrogen, extending the relevance of the material platform beyond VOC detection. The researchers proposed potential gas-sensing mechanisms, linking performance improvements to how metal nanoparticles and ZnO work together during adsorption and reaction at the surface.

This work was published in Frontiers of Materials Science and highlights a scalable, greener fabrication route for metal-modified ZnO sensors. By combining hydrothermal growth with UV-driven nanoparticle formation, the approach offers a practical pathway toward more reliable detection materials for complex atmospheric conditions.

Subject of Research: Experimental study
Article Title: Preparation of noble metal modified zinc oxide nanoflakes and their gas-sensing properties
News Publication Date: 23-Jun-2026
Web References: http://dx.doi.org/10.1007/s11706-026-0775-y
References: 10.1007/s11706-026-0775-y
Image Credits: HIGHER EDUCATION PRESS

Keywords

Physical sciences / Chemistry