Research status and prospect of purification technology of sulfur-containing odor gas.

Catalytic purification of sulphur-containing malodorous gases has attracted wide attention because of its advantages of high purification efficiency, low energy consumption and lack of secondary pollution. The selection of efficient catalysts is the key to the problem, while the preparation and optimisation of catalysts depend on the analysis of experimental results and in-depth mechanistic analysis. By analysing the published literature, bibliometric analysis can identify existing research hotspots, the areas of interest and predict development trends, which can help to identify hot catalysts in the catalytic purification of sulphur-containing odours and to investigate their catalytic purification mechanisms. Therefore, this paper uses bibliometric analysis, based on Web Of Science and CNKI databases, CiteSpace and VOS viewer software to collate and analyse the literature on the purification of sulphur-containing odour pollutants, to identify the current research hotspots, to summarise the progress of research on the catalytic purification of different types of sulphur-containing odours, and to analyse their reaction mechanisms and kinetics. On this basis, the research progress of catalytic purification of different kinds of sulfur odour is summarized, and the reaction mechanism and dynamics are summarized.

Adsorption removal of mercury from flue gas by metal selenide: A review.

Mercury (Hg) pollution has been a global concern in recent decades, posing a significant threat to entire ecosystems and human health due to its cumulative toxicity, persistence, and transport in the atmosphere. The intense interaction between mercury and selenium has opened up a new field for studying mercury removal from industrial flue gas pollutants. Besides the advantages of good Hg° capture performance and low secondary pollution of the mineral selenium compounds, the most noteworthy is the relatively low regeneration temperature, allowing adsorbent regeneration with low energy consumption, thus reducing the utilization cost and enabling recovery of mercury resources. This paper reviews the recent progress of mineral selenium compounds in flue gas mercury removal, introduces in detail the different types of mineral selenium compounds studied in the field of mercury removal, reviews the adsorption performance of various mineral selenium compounds adsorbents on mercury and the influence of flue gas components, such as reaction temperature, air velocity, and other factors, and summarizes the adsorption mechanism of different fugitive forms of selenium species. Based on the current research progress, future studies should focus on the economic performance and the performance of different carriers and sizes of adsorbents for the removal of Hg0 and the correlation between the gas-particle flow characteristics and gas phase mass transfer with the performance of Hg0 removal in practical industrial applications. In addition, it remains a challenge to distinguish the oxidation and adsorption of Hg0 quantitatively.

Analysis on typical characteristics and causes of coal mine gas explosion accidents in China.

Large-scale coal mine gas explosion (CMGE) accidents have occurred occasionally and exerted a devastating effect on society. Therefore, it is essential to systematically identify the characteristics and association rules of causes of CMGE accidents through analysis on large-scale CMGE accident reports. In this study, 298 large-scale CMGE accidents in China from 2000 to 2021 were taken as the data sample, and mathematical statistical methods were adopted to analyze their general characteristics, coupling cross characteristics, and characteristics of gas accumulation and ignition sources. Moreover, the text mining technology and the Apriori algorithm were used for exploring the formation mechanism of CMGE accidents, during which 46 main causal factors were identified and 59 strong association rules were obtained. Furthermore, an accident causation network was constructed based on the co-occurrence matrix. The key causal items and sets of CMGE accidents were clarified through network centrality analysis. According to the research results, electrical equipment failure, cable short circuit, mine lamp misfire, hot-line work, and blasting spark are the key ignition sources of CMGE. Fan failure, airflow short circuit, and local ventilation fan damage are the main causes of gas accumulation. Besides, the confidence levels of two association rules of "static spark-fan failure" and "blasting spark-airflow short circuit" are higher than 70%, indicating that they are the two dominant risk-coupling paths of gas explosions. In addition, six causes appear frequently in the shortest risk paths of gas explosion and are closely related to other causes, i.e., fan failure, local ventilation fan damage, static sparks, electrical equipment failure, self-heating ignition, and friction impact sparks. This study provides a new perspective on identifying causes of accidents and their complex association mechanisms from accident report data for practical guidance in risk assessment and accident prevention.

Simultaneous costs minimizing in electricity and gas micro-grids with the presence of distributed generation.

Distributed generation can actively participate in the day-ahead markets, real-time power balance, and wholesale gas markets to achieve various goals, such as supplying gas to various electric power generation plants. A multi-objective network with two types of loads is considered in this paper. The reason for the simultaneous optimization of these two networks is that these two energy carriers are dependent on each other and gas is needed to produce electricity, so this issue can be addressed with a multi-objective function. The simulation carried out in this article is coded in GAMS software as a mixed integer linear programming (MILP). The efficiency of gas turbines and fuel cells in this article is dependent on their working point, and considering the exact model of these resources and the relationships related to the calculation of their fuel consumption is non-linear. On the other hand, a binary variable has been used to show the charging and discharging state of the storage and the on-and-off state of the gas turbines. Therefore, the problem considered in this article is a MILP problem. The results of this article are the proper planning of charging and discharging of the energy storage system with the proper planning of the power generation of different energy sources considering the network loads in two optimized and non-optimized scenarios.

A biodegradable calcium sulfite nanoreactor for pH triggered gas therapy in combination with chemotherapy.

As a gasotransmitter, endogenous sulfur dioxide (SO2) plays an important role in cardiovascular regulation. In addition, excessive SO2 can react with overexpressed hydrogen peroxide (H2O2) in tumor cells to generate toxic radicals, which can induce severe oxidative damage to tumor cells and result in cell apoptosis. This highlights the potential of SO2 in oncotherapy. However, the limited availability of endogenous H2O2 and uncontrolled release of SO2 gas significantly impede the effectiveness of SO2 gas therapy. To address this challenge, a biodegradable calcium sulfite (CS) nanocarrier loaded with 10-hydroxycamptothecin (HCPT) was developed for tumor pH-triggered SO2 gas therapy in combination with chemotherapy. This nanoreactor could be degraded in an acidic tumor microenvironment to release SO2 gas and the HCPT drug. The released SO2 gas induced serious oxidative damage to tumor cells by depleting glutathione (GSH) and generating toxic radicals through a reaction with intracellular H2O2. Simultaneously, the HCPT drug promoted tumor cell apoptosis through chemotherapy and boosted SO2 gas therapy by elevating the H2O2 level within the tumor cells. Consequently, the combination of SO2 gas therapy and chemotherapy provided a promising approach for effective tumor treatment.

Adsorbtive removal of HF toxic gas via tinsulfide monolayer modification: A molecular perspective.

Hydrofluoric Acid (HF) is considered one of the most hazardous chemicals used in industrial plants. Even small exposures to HF can have fatal consequences if not promptly and properly treated. Various research teams have presented numerous substances with the objective of capturing or detecting toxic HF gas. In this study, we explore the impact of HF gas on a single layer of SnS by employing density functional theory (DFT). The interaction nature between the gas molecule and the adsorbent is elucidated by analyzing the related adsorption energy, electronic structure properties and differential charge transfer. The findings indicate that HF is physically adsorbed on the pristine SnS with an adsorption energy value of -0.63 eV. By introducing a Sn mono vacancy defect, the modification of SnS enhances the adsorption energy to -1.26 eV, resulting in a chemisorption process. Molecular fluorine (F2) was discovered to undergo a barrierless reaction with SnS, resulting in the formation of fluorine-substituted SnS. It has been discovered that the substitution of fluorine atoms enhances the reactivity of SnS towards hydro-gen fluoride gas. The adsorption potential of the studied structures towards HF gas was determined to be in the following order: F2SnS > VSn-SnS > VS-SnS âˆ¼ SnS. The current study is anticipated to offer new molecular insights that could lead to the creation of innovative devices for detecting or eliminating HF toxic gas from a specific atmosphere.

Nanotechnology based gas delivery system: a "green" strategy for cancer diagnosis and treatment.

Gas therapy, a burgeoning clinical treatment modality, has garnered widespread attention to treat a variety of pathologies in recent years. The advent of nanoscale gas drug therapy represents a novel therapeutic strategy, particularly demonstrating immense potential in the realm of oncology. This comprehensive review navigates the landscape of gases endowed with anti-cancer properties, including hydrogen (H2), carbon monoxide (CO), carbon dioxide (CO2), nitric oxide (NO), oxygen (O2), sulfur dioxide (SO2), hydrogen sulfide (H2S), ozone (O3), and heavier gases. The selection of optimal delivery vectors is also scrutinized in this review to ensure the efficacy of gaseous agents. The paper highlights the importance of engineering stimulus-responsive delivery systems that enable precise and targeted gas release, thereby augmenting the therapeutic efficiency of gas therapy. Additionally, the review examines the synergistic potential of integrating gas therapy with conventional treatments such as starvation therapy, ultrasound (US) therapy, chemotherapy, radiotherapy (RT), and photodynamic therapy (PDT). It also discusses the burgeoning role of advanced multimodal and US imaging in enhancing the precision of gas therapy applications. The insights presented are pivotal in the strategic development of nanomedicine platforms designed for the site-specific delivery of therapeutic gases, heralding a new era in cancer therapeutics.

Multi-gas pollutant detection based on sparrow search algorithm optimized ALSTM-FCN.

It is critical to identify and detect hazardous, flammable, explosive, and poisonous gases in the realms of industrial production and medical diagnostics. To detect and categorize a range of common hazardous gasses, we propose an attention-based Long Short term memory Full Convolutional network (ALSTM-FCN) in this paper. We adjust the network parameters of ALSTM-FCN using the Sparrow search algorithm (SSA) based on this, by comparison, SSA outperforms Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), Gray Wolf Optimization (GWO) Algorithm, Cuckoo Search (CS) Algorithm and other traditional optimization algorithms. We evaluate the model using University of California-Irvine (UCI) datasets and compare it with LSTM and FCN. The findings indicate that the ALSTM-FCN hybrid model has a better reliability test accuracy of 99.461% than both LSTM (89.471%) and FCN (96.083%). Furthermore, AdaBoost, logistic regression (LR), extra tree (ET), decision tree (DT), random forest (RF), K-nearest neighbor (KNN) and other models were trained. The suggested approach outperforms the conventional machine learning model in terms of gas categorization accuracy, according to experimental data. The findings indicate a potential for a broad range of polluting gas detection using the suggested ALSTM-FCN model, which is based on SSA optimization.

Anode potential regulates gas composition and microbiome in anaerobic electrochemical digestion.

Anaerobic electrochemical digestion (AED) is an effective system for recovering biogas from organic wastes. However, the effects of different anode potentials on anaerobic activated sludge remain unclear. This study confirmed that biofilms exhibited the best electroactivity at -0.2 V (vs. Ag/AgCl) compared to -0.4 V and 0 V. Gas was further regulated, with the highest hydrogen content (47 ± 7 %) observed at -0.2 V. The 0 V system produced the largest amount of methane (70 ± 8 %) and exhibited the greatest presence of hydrogen-utilizing microorganisms. The gas yield at -0.4 V was the lowest, with no hydrogen detected. Excess bioelectrohydrogen at -0.2 V and 0 V caused the co-enrichment of Methanobacterium and Acetoanaerobium, establishing a thermodynamically feasible current-acetate-hydrogen electron cycle to improve electrogenesis. These results provide insights into the regulatory strategies of MEC technology during anaerobic digestion, which play a decisive role in determining the composition of biogas.

Study of LFG bubble accumulation and discontinuous flow in the highly saturated region of landfill below the leachate level.

Landfills in developing countries are typically characterized by high waste water content and elevated leachate levels. Despite the ongoing biodegradation of waste in the highly saturated regions of these landfills, which leads to gas accumulation and bubble formation, the associated gas pressure that poses a risk to landfill stability is often overlooked. This paper introduces a landfill gas (LFG) bubble generation model and a two-fluid model that considers bubble buoyancy and porous medium resistance. The entire process can be divided into two stages based on the force balance and velocity of bubbles: Bubble Development Stage and the Two-Fluid Flow Stage. The models were validated using a one-dimensional analytical solution of hydraulic distribution that considers bubble generation, as well as an experiment involving air injection into a saturated medium. The mechanisms of LFG accumulation and ascent, leachate level rise, and discontinuous leachate-gas flow were then investigated in conjunction with continuous flow in the unsaturated region. The results indicate that the generation of LFG bubbles below the leachate level can cause a rise in the level height of more than 20%. During the Bubble Development Stage, there is a critical height for bubble ascent, above which the buoyancy exceeds the combined forces of gravity and resistance, resulting in less than 10% of bubbles continuously flowing into the unsaturated zone for recovery. The developed model effectively captures the accumulation and flow of LFG bubbles below the leachate level and could be further utilized to study leachate-gas pumping in the future.

[Research progress in bioconversion of C1 gases into oleochemicals].

The utilization of C1 gases (CH4, CO2, and CO) for the production of oleochemicals applied in the energy and platform chemicals through microbial engineering has emerged as a promising approach to reduce greenhouse gas emissions and decrease dependence on fossil fuel. C1 gas-utilizing microorganisms, such as methanotrophs, microalgae, and acetogens, are capable of converting C1 gases as the sole substrates for cell growth and oleochemical synthesis with different carbon-chain lengths, garnering considerable attention from both scientific community and industry field for sustainable biomanufacturing. This paper comprehensively reviews recent advancements in the development of engineered cell factories utilizing C1 gases for the production of oleochemicals, elucidating the key metabolic pathways of biosynthesis. Furthermore, this paper highlights the research progress and prospects in optimizing gene expression, metabolic pathway reconstruction, and fermentation conditions for efficient oleochemical production from C1 gases. This review provides valuable insights and guidance for the efficient utilization of C1 gases and the development of carbon cycling-based bioeconomy.

Protonation State-Induced Unfolding of Protein Secondary Structure in the Gas Phase.

The combination of infrared spectroscopy (IR) and ion mobility mass spectrometry (IM-MS) has revealed that protein secondary structures are retained upon transformation from aqueous solution to the gas phase under gentle conditions. Yet the details about where and how these structural elements are embedded in the gas phase remain elusive. In this study, we employ long time scale molecular dynamics (MD) simulations to examine the extent to which proteins retain their solution structures and the impact of protonation state on the stability of secondary structures in the gas phase. Our investigation focuses on two well-studied proteins, myoglobin and ß-lactoglobulin, representing typical helical and ß-sheet proteins, respectively. Our simulations accurately reproduce the experimental collision cross section (CCS) data measured by IM-MS. Based on accurately reproducing previous experimental collision cross section data and dominant secondary structural species obtained from IM-MS and IR, we confirm that both proteins largely retain their native secondary structural components upon passing from aqueous solution to the gas phase. However, we observe significant reductions in secondary structure contents (19.2 ± 1.2% for myoglobin and 7.3 ± 0.6% for ß-lactoglobulin) in specific regions predominantly composed of ionizable residues. Further mechanistic analysis suggests that alterations in protonation states of these residues after phase transition induce changes in their local interaction networks and backbone dihedral angles, which potentially promote the unfolding of secondary structures in the gas phase. We anticipate that similar protonation state induced unfolding may be observed in other proteins possessing distinct secondary structures. Further studies on a broader array of proteins will be essential to refine our understanding of protein structural behavior during the transition to the gas phase.

Efficient Dynamic Phase Splitting Driven by Centrifugal Force for CO2 Capture from Flue Gas using Biphasic Solvents.

Carbon dioxide (CO2) chemisorption using biphasic solvents has been regarded as a promising approach, but challenges remain in achieving efficient dynamic phase-splitting during practical implementation. To address this, the centrifugal force was innovatively adopted to enhance the coalescence and separation of immiscible fine droplets within the biphasic solvent. The comprehensive evaluation demonstrates that centrifugal phase-splitting shows outstanding separation efficiency (>95%) and excellent applicability for various solvents. Correlation analysis reveals a strong relationship between the rich phase's viscosity, lean phase's residual CO2, and the phase separation efficiency. The time-profile behavior of immiscible droplets, observed through microscope images of phase-splitting, enables the estimation of the growth and coalescence rates of the discrete phase. Industrial-scale process simulation for technical and economic analysis confirms that the total capture cost ($ 42.5/t CO2) can be reduced by ∼22% with the use of biphasic solvents and a centrifugal separator compared to conventional methods. This study introduces a fresh perspective on polarity-induced cluster generation and coagulation-induced separation, offering an effective solution to address the challenges associated with dynamic phase-splitting in biphasic solvents during practical applications.

Gas-Phase Structures of Fucosylated Oligosaccharides: Alkali Metal and Halogen Influences.

Fucosylated carbohydrate antigens play critical roles in physiology and pathology with function linked to their structural details. However, the separation and structural characterization of isomeric fucosylated epitopes remain challenging analytically. Here, we report for the first time the influence of alkali metal cations (Li+, Na+, K+, Rb+, and Cs+) and halogen anions (Cl-, Br-, and I-) on the gas-phase conformational landscapes of common fucosylated trisaccharides (Lewis A, X, and H types 1 and 2) and tetrasaccharides (Lewis B and Y) using trapped ion mobility spectrometry coupled to mass spectrometry and theoretical calculations. Inspection of the mobility profiles of individual standards showed a dependence on the number of mobility bands with the oligosaccharide and the alkali metal and halogen; collision cross sections are reported for all of the observed species. Results showed that trisaccharides (Lewis A, X, and H types 1 and 2) can be best mobility resolved in the positive mode using the [M + Li]+ molecular ion form (baseline resolution r ≈ 2.88 between Lewis X and A); tetrasaccharides can be best mobility resolved in the negative mode using the [M + I]- molecular ion form (baseline separation r ≈ 1.35 between Lewis B and Y). The correlation between the number of oligosaccharide conformers as a function of the molecular ion adduct was studied using density functional theory. Theoretical calculations revealed that smaller cations can form more stable structures based on the number of coordinations, while larger cations induced greater oligosaccharide reorganizations; candidate structures are proposed to better understand the gas-phase oligosaccharide rearrangement trends. Inspection of the candidate structures suggests that the interplay between ion size/charge density and molecular structure dictated the conformational preferences and, consequently, the number of mobility bands and the mobility separation across isomers. This work provides a fundamental understanding of the gas-phase structural dynamics of fucosylated oligosaccharides and their interaction with alkali metals and halogens.

Cooling positronium to ultralow velocities with a chirped laser pulse train.

When laser radiation is skilfully applied, atoms and molecules can be cooled1-3, allowing the precise measurements and control of quantum systems. This is essential for the fundamental studies of physics as well as practical applications such as precision spectroscopy4-7, ultracold gases with quantum statistical properties8-10 and quantum computing. In laser cooling, atoms are slowed to otherwise unattainable velocities through repeated cycles of laser photon absorption and spontaneous emission in random directions. Simple systems can serve as rigorous testing grounds for fundamental physics-one such case is the purely leptonic positronium11,12, an exotic atom comprising an electron and its antiparticle, the positron. Laser cooling of positronium, however, has hitherto remained unrealized. Here we demonstrate the one-dimensional laser cooling of positronium. An innovative laser system emitting a train of broadband pulses with successively increasing central frequencies was used to overcome major challenges posed by the short positronium lifetime and the effects of Doppler broadening and recoil. One-dimensional chirp cooling was used to cool a portion of the dilute positronium gas to a velocity distribution of approximately 1 K in 100 ns. A major advancement in the field of low-temperature fundamental physics of antimatter, this study on a purely leptonic system complements work on antihydrogen13, a hadron-containing exotic atom. The successful application of laser cooling to positronium affords unique opportunities to rigorously test bound-state quantum electrodynamics and to potentially realize Bose-Einstein condensation14-18 in this matter-antimatter system.

Investigation of gaseous end products produced by thulium fiber laser lithotripsy of cystine, uric acid, and calcium oxalate monohydrate stones: A gas chromatographic and electron microscopic analysis.

Laser lithotripsy mechanisms can cause the chemical decomposition of stone components and the emergence of different end products. However, the potentially toxic end products formed during thulium fiber laser (TFL) lithotripsy of cystine stones have not been sufficiently investigated. The aim of our in vitro study is to analyze the chemical content of the gas products formed during the fragmentation of cystine stone with TFL. Human renal calculi consisting of 100% pure cystine, calcium oxalate monohydrate, or uric acid were fragmented separately with TFL in experimental setups and observed for gas release. After the lithotripsy, only the cystine stones showed gas formation. Gas chromatography-mass spectrometry was used to analyze the gas qualitatively, and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) and X-ray diffraction was used to examine the dried cystine stone fragments. Fragmentation of the cystine stones released free cystine, sulfur, hydrogen sulfide, and carbon disulfide gas. The SEM-EDX and X-ray diffraction analyses revealed that the free cystine in the dried fragments contained 43.1% oxygen, 28.7% sulfur, 16.1% nitrogen, and 12.1% carbon atoms according to atomic weight. The detection of potentially toxic gases after lithotripsy of cystine stones with TFL indicates a risk of in vivo production. Awareness needs to be increased among healthcare professionals to prevent potential inhalation and systemic toxicity for patients and operating room personnel during TFL lithotripsy of cystine stones.

Improved medical waste plasma gasification modelling based on implicit knowledge-guided interpretable machine learning.

Ensuring the interpretability of machine learning models in chemical engineering remains challenging due to inherent limitations and data quality issues, hindering their reliable application. In this study, a qualitatively implicit knowledge-guided machine learning framework is proposed to improve plasma gasification modelling. Starting with a pre-trained machine learning model, parameters are further optimized by integrating the heuristic algorithm to minimize the data fitting errors and resolving implicit monotonic inconsistencies. The latter is comprehensively quantified through Monte Carlo simulations. This framework is adaptive to different machine learning techniques, exemplified by artificial neural network (ANN) and support vector machine (SVM) in this study. Validated by a case study on plasma gasification, the results reveal that the improved models achieve better generalizability and scientific interpretability in predicting syngas quality. Specifically, for ANN, the root mean square error (RMSE) and knowledge-based error (KE) reduce by 36.44% and 83.22%, respectively, while SVM displays a decrease of 2.58% in RMSE and a remarkable 100% in KE. Importantly, the improved models successfully capture all desired implicit monotonicity relationships between syngas quality and feedstock characteristics/operating parameters, addressing a limitation that traditional machine learning struggles with.

The UCI Fluxtron: A versatile dynamic chamber and software system for biosphere-atmosphere exchange research.

Here we present the UCI Fluxtron, a cost-effective multi-enclosure dynamic gas exchange system that provides an adequate level of control of the experimental conditions for investigating biosphere-atmosphere exchange of trace gases. We focus on the hardware and software used to monitor, control, and record the air flows, temperatures, and valve switching, and on the software that processes the collected data to calculate the exchange flux of trace gases. We provide the detailed list of commercial materials used and also the software code developed for the Fluxtron, so that similar dynamic enclosure systems can be quickly adopted by interested researchers. Furthermore, the two software components -Fluxtron Control and Fluxtron Process- work independently of each other, thus being highly adaptable for other experimental designs. Beyond plants, the same experimental setup can be applied to the study of trace gas exchange by animals, microbes, soil, or any materials that can be enclosed in a suitable container.

Unlocking the techno-economic potential of biomass gasification for power generation: Comparative analysis across diverse plant capacities.

This research aims to evaluate the techno-economic viability and commercial potential of biomass gasification across different capacities. Sensitivity analysis was conducted based on an established downdraft gasifier model using Aspen Plus. Results underscored the significant impact of gasification temperature and equivalence ratio (ER) on syngas composition, low heating value (LHV), and cold gas efficiency (CGE). Among the feedstocks tested, coconut shell emerged as a feasible feedstock, yielding syngas with an LHV of 8.93 MJ/Nm3 and achieving a CGE of up to 71.12 %. Optimal gasification temperatures ranged between 750 °C to 1,000 °C, with peak ER falling within 0.1 to 0.3. Economic analysis revealed that smaller-scale operations like Plant A resulted in a negative net present value of - US$0.63 million, indicating unfavorable investments. The internal rate of return notably increased from 9.53 % for Plant B compared to -2.56 % for Plant A (20 kW). Plant D, with larger capacity of 20 MW, showed an impressive payback period of less than two years (1.69 years). Medium to large-scale plants such as Plant C (2 MW) and Plant D demonstrated greater economic resilience, with Plant D achieving a significantly lower levelized cost of electricity of US$ 0.19/kWh compared to Plant A at US$ 0.86/kWh. It was noted that the impact of capital costs, operating expenses, and revenue variations is less pronounced at larger scales. The findings from this study shed light on the feasibility of biomass gasification for power generation as a viable option, thereby unlocking the potential for its large-scale commercialization.

Exploring the manganese-dependent interaction between a transcription factor and its corresponding DNA: insights from gas-phase electrophoresis on a nES GEMMA instrument.

Manganese ion homeostasis is vital for bacteria and is achieved via manganese-dependent transcription factors. Manganese mediation of transcription factor attachment to the corresponding oligonucleotide sequences can be investigated, e.g. via electrophoretic mobility shift assays (EMSA). Formation of specific biocomplexes leads to differences in the migration pattern upon gel electrophoresis. Focusing on electrophoresis in the gas-phase, applying a nano electrospray gas-phase electrophoretic mobility molecular analyzer (nES GEMMA) also known as nES differential mobility analyzer (nES DMA), and on transcription factors (MntR proteins) from Bacillus subtilis and Mycobacterium tuberculosis, we took interest in the gas-phase electrophoresis of the corresponding biospecific complexes. We compared nES GEMMA, separating analytes in the nanometer regime (a few to several hundred nm in diameter) in the gas-phase in their native state according to particle size, to EMSA data. Indeed we were able to demonstrate manganese-mediated attachment of MntR to target genomic sequences with both analytical techniques. Despite some inherent pitfalls of the nES GEMMA method like analyte/instrument surface interactions, we were able to detect the target complexes. Moreover, we were able to calculate the molecular weight (MW) of the obtained species by application of a correlation function based on nES GEMMA obtained data. As gas-phase electrophoresis also offers the possibility of offline hyphenation to orthogonal analysis techniques, we are confident that nES GEMMA measurements are not just complementary to EMSA, but will offer the possibility of further in-depth characterization of biocomplexes in the future.