Slow-Light-Enhanced Polarization Division Multiplexing Infrared Absorption Spectroscopy for On-Chip Wideband Multigas Detection in a 1D Photonic Crystal Waveguide.

Slow-light photonic crystal waveguide (PCW) gas sensors based on infrared absorption spectroscopy play a pivotal role in enhancing the on-chip interaction between light and gas molecules, thereby significantly boosting sensor sensitivity. However, two-dimensional (2D) PCWs are limited by their narrow mode bandwidth and susceptibility to polarization, which restricts their ability for multigas measurement. Due to quasi-TE and quasi-TM mode guiding characteristics in one-dimensional (1D) PCW, a novel slow-light-enhanced polarization division multiplexing infrared absorption spectroscopy was proposed for on-chip wideband multigas detection. The optimized 1D PCW gas sensor experimentally shows an impressive slow-light mode bandwidth exceeding 100 nm (TM, 1500-1550 nm; TE, 1610-1660 nm) with a group index ranging from 4 to 25 for the two polarizations. The achieved bandwidth in the 1D PCW is 2-3 times that of the reported quasi-TE polarized 2D PCWs. By targeting the absorption lines of different gas species, multigas detection can be realized by modulating the lasers and demodulating the absorption signals at different frequencies. As an example, we performed dual-gas measurements with the 1D PCW sensor operating in TE mode at 1.65 µm for methane (CH4) detection and in TM mode at 1.53 µm for acetylene (C2H2) detection. The 1 mm long sensor achieved a remarkable limit of detection (LoD) of 0.055% for CH4 with an averaging time of 17.6 s, while for C2H2, the LoD was 0.18%. This polarization multiplexing sensor shows great potential for on-chip gas measurement because of the slow-light enhancement in the light-gas interaction effect as well as the large slow-light bandwidth for multigas detection.

Composition design and optimization of Fe-C-Mn-Al steel based on machine learning.

The purpose of this study is to explore the composition space of Fe-C-Mn-Al steel using machine learning in order to identify materials with high-strength mechanical properties. A dataset of 580 steel samples was collected from the literature, each containing information on elemental composition, heat treatment processes, specimen dimensions, and mechanical properties (ultimate tensile strength and total elongation). Eight common machine learning models were constructed to predict the ultimate tensile strength (UTS) and total elongation (TE) of the steel. It was observed that the random forest regression (RFR) model, when trained, demonstrated superior overall performance in predicting UTS, with an average absolute error of approximately 90 MPa, and TE, with an average absolute error of about 7.9%. Validation of the model using eight sets of data that were not part of the dataset revealed that the predictions were in close agreement with experimental results, indicating the strong predictive capability of the RFR model. Subsequently, the trained RFR model was used to explore the composition space of Fe-C-Mn-Al steel, identifying the top fifty combinations of elemental compositions and heat treatment parameters, all of which manifest high ultimate tensile strength (UTS). This provides valuable research directions and methods to expedite the development of high-strength Fe-C-Mn-Al steel.

Development of a mid-infrared sensor system for early fire identification in cotton harvesting operations.

To realize early fire identification in cotton harvesting operations, a mid-infrared carbon monoxide (CO) sensor system was developed. To match the broadband light source with a 15° divergence angle, a multipass gas cell (MPGC) with an effective path length of 180 cm was designed to improve sensor sensitivity, leading to a limit of detection (LoD) of 0.83 parts-per-million by volume (ppmv). A damping module with springs at the bottom and front/back sides was fabricated, which can effectively reduce the vibration intensity by >80%. The sensor system can operate normally from -40 °C to 85 °C by stabilizing the temperature of the optical module through heating or cooling as well as using automotive electronic components. An adaptive early fire identification algorithm based on a dual-parameter threshold alarming method was proposed to avoid false and missing alarms. Field deployments on a harvester verified the good practicability of the sensor system.

A Fast Adaptive Multi-Scale Kernel Correlation Filter Tracker for Rigid Object.

The efficient and accurate tracking of a target in complex scenes has always been one of the challenges to tackle. At present, the most effective tracking algorithms are basically neural network models based on deep learning. Although such algorithms have high tracking accuracy, the huge number of parameters and computations in the network models makes it difficult for such algorithms to meet the real-time requirements under limited hardware conditions, such as embedded platforms with small size, low power consumption and limited computing power. Tracking algorithms based on a kernel correlation filter are well-known and widely applied because of their high performance and speed, but when the target is in a complex background, it still can not adapt to the target scale change and occlusion, which will lead to template drift. In this paper, a fast multi-scale kernel correlation filter tracker based on adaptive template updating is proposed for common rigid targets. We introduce a simple scale pyramid on the basis of Kernel Correlation Filtering (KCF), which can adapt to the change in target size while ensuring the speed of operation. We propose an adaptive template updater based on the Mean of Cumulative Maximum Response Values (MCMRV) to alleviate the problem of template drift effectively when occlusion occurs. Extensive experiments have demonstrated the effectiveness of our method on various datasets and significantly outperformed other state-of-the-art methods based on a kernel correlation filter.

Light-induced off-axis cavity-enhanced thermoelastic spectroscopy in the near-infrared for trace gas sensing.

A trace gas sensing technique of light-induced off-axis cavity-enhanced thermoelastic spectroscopy (OA-CETES) in the near-infrared was demonstrated by combing a high-finesse off-axis integrated cavity and a high Q-factor resonant quartz tuning fork (QTF). Sensor parameters of the cavity and QTF were optimized numerically and experimentally. As a proof-of-principle, we employed the OA-CETES for water vapor (H2O) detection using a QTF (Q-factor ∼12000 in atmospheric pressure) and a 10cm-long Fabry-Perot cavity (finesse ∼ 482). By probing a H2O line at 7306.75 cm-1, the developed OA-CETES sensor achieved a minimum detection limit (MDL) of 8.7 parts per million (ppm) for a 300 ms integration time and a normalized noise equivalent absorption (NNEA) coefficient of 4.12 × 10-9cm-1 WHz-1/2. Continuous monitoring of indoor and outdoor atmospheric H2O concentration levels was performed for verifying the sensing applicability. The realization of the proposed OA-CETES technique with compact QTF and long effective path cavity allows a class of optical sensors with low cost, high sensitivity and potential for long-distance and multi-point sensing.

Association study between genetic variants and the risk of schizophrenia in the Chinese population based on GWAS-implicated 6p21.3-23.1 human genome region: a case-control study.

BACKGROUND: Schizophrenia is a polygenic disease; however, the specific risk genetic variants of schizophrenia are still largely unknown. Single nucleotide polymorphism (SNP) is important genetic factor for the susceptibility of schizophrenia. Investigating individual candidate gene contributing to disease risk remains important. METHODS: In a case-control study, five SNPs located in 6p21.3-p23.1 including rs2021722 in human leukocyte antigen (HLA) locus and rs107822, rs383711, rs439205 and rs421446 within the upstream of microRNA-219a-1 were genotyped in 454 schizophrenia patients and 445 healthy controls to investigate the possible association between the loci and schizophrenia in a Han Chinese population. RESULTS: Our results showed significant associations between the rs2021722 and schizophrenia in allele (A vs. G: adjusted OR = 1.661, 95%CI = 1.196-2.308), co-dominant (AG vs. GG: OR = 1.760, 95%CI = 1.234-2.510) and dominant genetic model (AG + AA vs. GG: OR = 1.756, 95%CI = 1.237-2.492), respectively. Haplotype analysis showed that TGGT and CAAC were protective factor for schizophrenia compared with TAAC haplotype (OR = 0.324, 95% CI = 0.157-0.672; OR = 0.423, 95% CI = 0.199-0.900). CONCLUSIONS: These findings indicate that rs2021722 in HLA locus might be involved in pathogenesis of schizophrenia and that genotypes AG and allele A of the locus are risk factors for schizophrenia in the Han Chinese population, confirming the association between immune system and schizophrenia.

Concentration-Dependent Enhancing Effect of Dissolved Silicate on the Oxidative Degradation of Sulfamethazine by Zero-Valent Iron under Aerobic Conditions.

Dissolved silicate, as a ubiquitous inorganic component in natural waters, is reported to depress the reactivity of zero-valent iron (ZVI) for reductive reactions under anoxic conditions, but it is unclear if the same inhibitory effect occurs for a ZVI/O2 system. In this study, the role of dissolved silicate for the reactivity of micron-sized ZVI (mZVI) was revisited under aerobic conditions, and different observations were found. Silicate had a volcano-type enhancing effect on the performance of the ZVI/O2 system for sulfamethazine (SMT) degradation. The results showed that, under a circum-neutral or alkaline pH condition (pH 6.0-9.0), the presence of dissolved silicate could significantly enhance the degradation of SMT because silicate coordinated with ferrous ions and further led to the generation of reactive oxygen species (ROS). This study suggests that silicate can act as both a ligand and corrosion inhibitor in a ZVI/O2 system: the coordination of silicate and ferrous iron accelerated the oxidative degradation of organic pollutants in an oxic aqueous solution, while the corrosion inhibitory effect of surface-bound silicate at higher concentrations may decrease the reactivity of the ZVI/O2 system, thereby offsetting the enhancing effect from the silicate-coordinated ferrous iron. This study not only redefines the role of naturally occurring silicate for a ZVI reaction system but also gives clues to develop high-efficiency ZVI/O2 technologies for water remediation.

Real Time Monitoring of the Dynamic Intracluster Diffusion of Single Gold Atoms into Silver Nanoclusters.

Alloying metal materials with heterometal atoms is an efficient way to diversify the function of materials, but in-depth understanding of the dynamic heterometallic diffusion inside the alloying materials is rather limited, especially at the atomic level. Here, we report the real-time monitoring of the dynamic diffusion process of a single gold (Au) atom into an atomically precise silver nanocluster (Ag NC), Ag25(MHA)18 (MHA = 6-mercaptohexanoic acid), by using in situ UV-vis absorption spectroscopy in combination with mass and tandem mass spectrometry. We found that the Au heteroatom first replaces the Ag atom at the surface Ag2(MHA)3 motifs of Ag25(MHA)18. After that, the Au atom diffuses into the surface layer of the icosahedral Ag13 kernel and finally occupies the center of the alloy NCs to form the thermodynamically stable Au@Ag24(MHA)18 product. Density functional theory (DFT) calculations reveal that the key thermodynamic driving force is the preference of the Au heteroatom for the central site of alloy NCs. The real-time monitoring approach developed in this study could also be extended to other metal alloy systems to reveal the reaction dynamics of intracluster diffusion of heteroatoms, as well as the formation mechanisms of metal alloy nanomaterials.

A near-infrared C2H2/CH4 dual-gas sensor system combining off-axis integrated-cavity output spectroscopy and frequency-division-multiplexing-based wavelength modulation spectroscopy.

By combining frequency division multiplexing assisted wavelength modulation spectroscopy (FDM-WMS) and off-axis integrated-cavity output spectroscopy (OA-ICOS), a near-infrared (near-IR) dual-gas sensor system was demonstrated for simultaneous chemical gas-phase detection of acetylene (C2H2) and methane (CH4). Two distributed feedback (DFB) lasers modulated at the frequency of 3 kHz and 4 kHz with an emitting wavelength of 1532 and 1653 nm were used to target two absorption lines, C2H2 at 6523.88 cm-1 and CH4 at 6046.95 cm-1, respectively. A 6 cm-long cavity was fabricated, which reveals an effective path length of 9.28 m (@1532 nm, C2H2) and 8.56 m (@1653 nm, CH4), respectively. Performances of the dual-gas sensor system were experimentally evaluated using C2H2 and CH4 samples generated by an Environics gas mixing system. An Allan deviation of 700 parts-per-billion in volume (ppbv) for C2H2 with an averaging time of 200 s and 850 ppbv for CH4 with an averaging time of 150 s was achieved for these two gas species. Dynamic measurements of a C2H2/CH4 : N2 mixture were performed for monitoring both C2H2 and CH4 simultaneously. This dual-gas sensor has the merits of reduced size and cost compared to two separate OA-ICOS sensors and reveals the minimum detectable column density (DCD) compared to other reported C2H2 and CH4 sensor systems.

Understanding the electrode reaction process of dechlorination of 2,4-dichlorophenol over Ni/Fe nanoparticles: Effect of pH and 2,4-dichlorophenol concentration.

Herein, with the exploitation of iron and nickel electrodes, the 2,4-dichlorophenol (2,4-DCP) dechlorinating processes at the anode and cathode, respectively, were separately studied via various electrochemical techniques (e.g., Tafel polarization, linear polarization, electrochemical impedance spectroscopy). With this in mind, Ni/Fe nanoparticles were prepared by chemical solution deposition, and utilized to test the dechlorination activities of 2,4-DCP over a bimetallic system. For the iron anode, the results showed that higher 2,4-DCP concentration and solution acidity aggravated the corrosion within the electrode. The charge transfer resistance (Rct) values of the iron electrode were 703, 473, 444, and 437 Ω∙cm2 for the initial 2,4-DCP concentrations of 0, 20, 50, and 80 mg/L, respectively. When the bulk pH of the 2,4-DCP solution varied from 3.0, 5.0 to 7.0, the corresponding Rct values were 315, 376, and 444 Ω∙cm2, respectively. For the nickel cathode, the reduction current densities on the electrode at -0.75 V (vs. saturated calomel electrode) were 80, 106, and 111 µA/cm2, for initial 2,4-DCP concentrations of 40, 80, and 125 mg/L. The dechlorination experiments demonstrated that when the initial pH of the solution was 7.0, 5.0, and 3.0, the dechlorination percentage of 2,4-DCP by Ni/Fe nanoparticles was 62%, 69%, and 74%, respectively, which was in line with the electrochemical experiments. 10 wt.% Ni loading into Ni/Fe bimetal was affordable and gave a good dechlorination efficiency of 2,4-DCP, and fortunately the Ni/Fe nanoparticles remained comparatively stable in the dechlorination processes at pH 3.0.

Near-infrared acetylene sensor system using off-axis integrated-cavity output spectroscopy and two measurement schemes.

For highly sensitive and accurate acetylene (C2H2) detection, a near-infrared (NIR) off-axis integrated-cavity output spectroscopy (OA-ICOS) sensor system based on an ultra-compact cage-based absorption cell was proposed. The absorption cell with dimensions of 10 cm × 8 cm × 6 cm realized a dense-pattern and an easily-aligned stable optical system. The OA-ICOS sensor system employed a 6cm-long optical cavity that was formed by two mirrors with a reflectivity of 99.35% and provided an effective absorption path length of ∼9.28 m. The performance of the C2H2 sensor system based on two measurement schemes, i.e. laser direct absorption spectroscopy (LDAS) and wavelength modulation spectroscopy (WMS) is reported. A NIR distributed feedback (DFB) laser was employed for targeting a C2H2 absorption line at 6523.88 cm-1. An Allan deviation analysis yielded a detection sensitivity of 760 parts-per-billion in volume (ppbv) for an averaging time of 304 s using the LDAS-based OA-ICOS. A detection sensitivity of 85 ppbv for an averaging time of 250 s was obtained using the WMS-based OA-ICOS, which was further improved by a factor of ~9 compared to the result obtained with the LDAS method. The proposed sensor system has the advantages of reduced size and cost with acceptable detection sensitivity, which is suitable for applications in trace gas sensing in harsh environments and weight-limited balloon-embedded observations.

Near-infrared broadband cavity-enhanced sensor system for methane detection using a wavelet-denoising assisted Fourier-transform spectrometer.

The majority of broadband cavity-enhanced systems are used to detect trace gas species in the visible spectral range. We demonstrated a broadband cavity-enhanced sensor system in combination with a Fourier-transform spectrometer (FTS) in the near-infrared (near-IR) region for methane (CH4) detection. Light from a tungsten-halogen lamp was coupled into a high-finesse cavity and the light leaking from the cavity was imaged onto the FTS. An optimal incident beam diameter of 2.25 cm was required in the condition of a 40 cm-long cavity of a 2.5 cm diameter and a 100 cm radius of curvature (RoC) mirror. The CH4 sensor system was capable of operating at a temperature of 300 K and 1 atm gas pressure. Based on an Allan variance analysis, a minimum detectable absorption coefficient of 4.6 × 10-7 cm-1 was achieved. A wavelet denoising (WD) method was introduced in the retrieval of the gas concentration, which improved the measurement precision from 10.2 parts-per-million in volume (ppmv) to 5.3 ppmv with an enhancement factor of 2 for a 20 min averaging time. The near-IR broadband cavity-enhanced sensor system can also be used to measure high-resolution absorption spectra of other atmospheric trace gas species.

Review of Incoherent Broadband Cavity-Enhanced Absorption Spectroscopy (IBBCEAS) for Gas Sensing.

Incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS) is of importance for gas detection in environmental monitoring. This review summarizes the unique properties, development and recent progress of the IBBCEAS technique. Principle of IBBCEAS for gas sensing is described, and the development of IBBCEAS from the perspective of system structure is elaborated, including light source, cavity and detection scheme. Performances of the reported IBBCEAS sensor system in laboratory and field measurements are reported. Potential applications of this technique are discussed.

Molten salt CO2 capture and electro-transformation (MSCC-ET) into capacitive carbon at medium temperature: effect of the electrolyte composition.

Electrochemical transformation of CO2 into functional materials or fuels (i.e., carbon, CO) in high temperature molten salts has been demonstrated as a promising way of carbon capture, utilisation and storage (CCUS) in recent years. In a view of continuous operation, the electrolysis process should match very well with the CO2 absorption kinetics. At the same time, in consideration of the energy efficiency, a molten salt electrochemical cell running at lower temperature is more beneficial to a process powered by the fluctuating renewable electricity from solar/wind farms. Ternary carbonates (Li : Na : K = 43.5 : 31.5 : 25.0) and binary chlorides (Li : K = 58.5 : 41.5), two typical kinds of eutectic melt with low melting points and a wide electrochemical potential window, could be the ideal supporting electrolyte for the molten salt CO2 capture and electro-transformation (MSCC-ET) process. In this work, the CO2 absorption behaviour in Li2O/CaO containing carbonates and chlorides were investigated on a home-made gas absorption testing system. The electrode processes as well as the morphology and properties of carbon obtained in different salts are compared to each other. It was found that the composition of molten salts significantly affects the absorption of CO2, electrode processes and performance of the product. Furthermore, the relationship between the absorption and electro-transformation kinetics are discussed based on the findings.

Storage of gold nanoclusters in muscle leads to their biphasic in vivo clearance.

Ultrasmall gold nanoclusters (Au NCs) show great potential in biomedical applications. Long-term biodistribution, retention, toxicity, and pharmacokinetics profiles are pre-requisites in their potential clinical applications. Here, the biodistribution, clearance, and toxicity of one widely used Au NC species-glutathione-protected Au NCs or GSH-Au NCs-are systematically investigated over a relatively long period of 90 days in mice. Most of the Au NCs are cleared at 30 days post injection (p.i.) with a major accumulation in liver and kidney. However, it is surprising that an abnormal increase of the Au amount in the heart, liver, spleen, lung, and testis is observed at 60 and 90 days p.i., indicating that the injected Au NCs form a V-shaped time-dependent distribution profile in various organs. Further investigations reveal that Au NCs are steadily accumulating in the muscle in the first 30 days p.i., and the as-stored Au NCs gradually release into the blood in 30-90 days p.i., which induces a re-distribution and re-accumulation of Au NCs in all blood-rich organs. Further hematology and biochemistry studies show that the re-accumulation of Au NCs still causes some liver toxicity at 30 days p.i. The muscle storage and subsequent release may give rise to the potential accumulation and toxicity risk of functional nanomaterials over long periods of time.

Microelectronic printed chitosan/chondroitin sulfate/ZnO flexible and environmentally friendly triboelectric nanogenerator.

The triboelectric nanogenerator (TENG) of natural biomaterials is a new type of energy harvesting device and can be used as a self-powered sensor, which has received extensive research and attention. In this paper, based on the biocompatibility of chitosan and chondroitin sulfate, ZnO-modified chitosan/chondroitin sulfate/ZnO TENG was prepared for research on wearable devices and sustainable power supply devices. This study employs molecular dynamics to compute the interaction energy between chitosan and ZnO molecules. Theoretical calculations have unequivocally substantiated the occurrence of a binding interaction between these two molecular entities. The effect of ZnO on chitosan/chondroitin sulfate morphology was investigated by atomic force microscopy. The chitosan/chondroitin sulfate/ZnO TENG has high flexibility and electrical output performance. It can reach 105 V and 3.3 µA of open-circuit voltage and short-circuit current. Chitosan/chondroitin Sulfate/ZnO TENG successfully converts the mechanical energy of human motion into electrical energy. Strong electrical signals are exhibited when making fists and waving fingers and wrists. The TENG is a self-powered source and lights up 70 blue light-emitting diodes (LEDs). The chitosan/chondroitin sulfate/ZnO TENG has demonstrated its capabilities in energy harvesting and wearable self-powered sensors.

An exhaled breath gas sensor system for near-infrared ammonia measurement and kidney diagnostics.

Breath analysis enables rapid, noninvasive diagnosis of human health by identifying and quantifying exhaled biomarker. Here, we demonstrated an exhaled breath sensing method using the near-infrared laser spectroscopy, and sub parts-per-million (ppm) level ammonia detection inside the exhaled gas was achieved employing a distributed feedback laser centered at 1512 nm and Kalman filtering algorithm. Integration of the ammonia sensor was realized for exhaled breath analysis of kidney patients, and a dual operation mechanism with static and dynamic modes was proposed to make this method applicable for real-time and comprehensive pre-diagnosis of kidney disease.

Exploration of resting-state brain functional connectivity as preclinical markers for arousal prediction in prolonged disorders of consciousness: A pilot study based on functional near-infrared spectroscopy.

BACKGROUND: There is no diagnostic assessment procedure with moderate or strong evidence of use, and evidence for current means of treating prolonged disorders of consciousness (pDOC) is sparse. This may be related to the fact that the mechanisms of pDOC have not been studied deeply enough and are not clear enough. Therefore, the aim of this study was to explore the mechanism of pDOC using functional near-infrared spectroscopy (fNIRS) to provide a basis for the treatment of pDOC, as well as to explore preclinical markers for determining the arousal of pDOC patients. METHODS: Five minutes resting-state data were collected from 10 pDOC patients and 13healthy adults using fNIRS. Based on the concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the time series, the resting-state cortical brain functional connectivity strengths of the two groups were calculated, and the functional connectivity strengths of homologous and heterologous brain networks were compared at the sensorimotor network (SEN), dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), frontoparietal network (FPN), and visual network (VIS) levels. Univariate binary logistic regression analyses were performed on brain networks with statistically significant differences to identify brain networks associated with arousal in pDOC patients. The receiver operating characteristic (ROC) curves were further analyzed to determine the cut-off value of the relevant brain networks to provide clinical biomarkers for the prediction of arousal in pDOC patients. RESULTS: The results showed that the functional connectivity strengths of oxyhemoglobin (HbO)-based SEN∼SEN, VIS∼VIS, DAN∼DAN, DMN∼DMN, SEN∼VIS, SEN∼FPN, SEN∼DAN, SEN∼DMN, VIS∼FPN, VIS∼DAN, VIS∼DMN, HbR-based SEN∼SEN, and SEN∼DAN were significantly reduced in the pDOC group and were factors that could reflect the participants' state of consciousness. The cut-off value of resting-state functional connectivity strength calculated by ROC curve analysis can be used as a potential preclinical marker for predicting the arousal state of subjects. CONCLUSION: Resting-state functional connectivity strength of cortical networks is significantly reduced in pDOC patients. The cut-off values of resting-state functional connectivity strength are potential preclinical markers for predicting arousal in pDOC patients.

Near-Infrared Off-Axis Cavity-Enhanced Optical Frequency Comb Spectroscopy for CO2/CO Dual-Gas Detection Assisted by Machine Learning.

Cavity-enhanced direct frequency comb spectroscopy (CE-DFCS) is widely used as a highly sensitive gas sensing technology in various gas detection fields. For the on-axis coupling incidence scheme, the detection accuracy and stability are seriously affected by the cavity-mode noise, and therefore, stable operation inevitably requires external electronic mode-locking and sweeping devices, substantially increasing system complexity. To address this issue, we propose off-axis cavity-enhanced optical frequency comb spectroscopy from both theoretical and experimental aspects, which is applied to the detection of single- and dual-gas of carbon monoxide (CO) and carbon dioxide (CO2) in the near-infrared. An erbium-doped fiber frequency comb with a repetition frequency of ∼41.709 MHz is coupled into a resonant cavity with a length of ∼360 mm in an off-axis manner, exciting numerous high-order modes to effectively suppress cavity-mode noise. The performance of multiple machine learning models is compared for the inversion of a single/dual gas concentration. A few absorbance spectra are collected to build a sample data set, which is then utilized for model training and learning. The results demonstrate that the Particle Swarm Optimization Support Vector Machine (PSO-SVM) model achieves the highest predictive accuracy for gas concentration and is ultimately applied to the detection system. Based on Allan deviation, the detection limit for CO in single-gas detection can reach 8.247 parts per million by volume (ppmv) by averaging 87 spectra. Meanwhile, for simultaneous CO2/CO measurement with highly overlapping absorbance spectra, the LoD can be reduced to 13.196 and 4.658 ppmv, respectively. The proposed optical gas sensing technique indicates the potential for the development of a field-deployable and intelligent sensor system capable of simultaneous detection of multiple gases.

Fine-tuning pore size by shifting coordination sites of ligands and surface polarization of metal-organic frameworks to sharply enhance the selectivity for CO2.

Based upon the (3,6)-connected metal-organic framework {Cu(L1)·2H(2)O·1.5DMF}(∞) (L1 = 5-(pyridin-4-yl)isophthalic acid) (SYSU, for Sun Yat-Sen University), iso-reticular {Cu(L2)·DMF}(∞) (L2 = 5-(pyridin-3-yl)isophthalic acid) (NJU-Bai7; NJU-Bai for Nanjing University Bai group) and {Cu(L3)·DMF·H(2)O}(∞) (L3 = 5-(pyrimidin-5-yl)isophthalic acid) (NJU-Bai8) were designed by shifting the coordination sites of ligands to fine-tune pore size and polarizing the inner surface with uncoordinated nitrogen atoms, respectively, with almost no changes in surface area or porosity. Compared with those of the prototype SYSU, both the adsorption enthalpy and selectivity of CO(2) for NJU-Bai7 and NJU-Bai8 have been greatly enhanced, which makes NJU-Bai7 and NJU-Bai8 good candidates for postcombustion CO(2) capture. Notably, the CO(2) adsorption enthalpy of NJU-Bai7 is the highest reported so far among the MOFs without any polarizing functional groups or open metal sites. Meanwhile, NJU-Bai8 exhibits high uptake of CO(2) and good CO(2)/CH(4) selectivity at high pressure, which are quite valuable characteristics in the purification of natural gases.