Multiple Transformations Research Articles

Phase Transformation, Microstructural Evolution and Tensile Properties of a TiH2-Based Powder Metallurgy Pure Titanium

Multiple phase transformations were carried out during dehydrogenation process of TiH2-based powder metallurgy. The influence of phase transformation on the microstructure is highly worthy of attention. In situ synchrotron radiation was employed to investigate the phase transformation sequence of TiH2 powder compact during the vacuum sintering process. It was found that a transformation route TiH1.971 → TiH2 + TiH + TiH0.71 + α(H) → TiH + TiH0.71 + α(H) → TiH + TiH0.71 + α(H) + β(H) → α(H) → α took place, resulting in an equiaxed microstructure. Increasing heating rate and avoiding the intense dehydrogenation to retain hydrogen-rich β (β(H)) and TiHx aciculae at the interfaces is found to be a feasible method to fabricate hierarchical α-Ti structures. A fully dense fine martensitic microstructure was produced after fast heating the TiH2 powder compact to 1100 °C and an immediate hot extrusion. Subsequently, by vacuum annealing treatment at 700 °C, composite α/βt lamellar structures were generated and a simultaneously enhanced tensile strength of 746 MPa and excellent elongation to fracture of 33.8% were achieved. It is suggested that adjusting the dehydrogenation reactions of TiH2-based powder metallurgy is conductive to generating hierarchical lamellar structures with a highly promising combination of strength and ductility for pure Ti.

An extraordinary case of Buschke–Lowenstein tumor: multiple localization, malignant transformation, and clinical insights—a case presentation and literature review

BackgroundBuschke–Lowenstein (B–L) tumors are rare, aggressive warty lesions that can grow to enormous sizes and have a risk of malignant transformation despite their benign histology. These tumors are primarily caused by HPV types 6 and 11, with additional risk factors, including immunodeficiency, multiple sexual partners, poor hygiene, and a history of other genital infections. The typical treatments include topical medications, cryotherapy, chemotherapy, radiotherapy, and surgery. Surgery is considered the gold standard, particularly for extensive and infiltrative lesions, as demonstrated in our case.Case presentationWe report a case of a male patient with perianal and penopubic B–L tumors, who had multiple sexual partners and no history of homosexual relationships, presenting with refractory classic condyloma. The perianal tumor exhibited malignant transformation, while the penopubic tumor remained histopathologically benign. Upon admission, the patient showed unusual biochemical blood results, including hypercalcemia and anemia of chronic illness. The operation and postoperative period were managed by a multidisciplinary team. The patient was satisfied with both the cosmetic and functional outcomes. Histopathological examination confirmed the suprapubic lesion as a B–L tumor, whereas the perianal specimen revealed a small focus of squamous cell carcinoma arising from a B–L tumor.ConclusionsWith an experienced multidisciplinary team, excision of the tumor is still the standard treatment for BL tumor. Achieving a negative surgical margin for any SCC foci is of utmost importance to prevent recurrence. Close and careful follow-up is essential due to the high recurrence rate associated with these tumors. Further research is needed to clarify existing controversies and to enrich the literature on this rare phenomenon.

Chemical Synthesis of 6-Azido-6-Deoxy Derivatives of Phosphatidylinositol Containing Different Fatty Acid Chains

This paper describes the synthesis of two 6-azido-6-deoxy derivatives of phosphatidylinositol (PI), which contained different fatty acid chains. These syntheses, starting from methyl α-d-glucopyranoside, employed multiple regioselective transformations with Ferrier rearrangement as one of the key steps. The PI derivatives contained different fatty acid chains in the lipids and an azido group in the inositol residue to facilitate their further functionalization under bioorthogonal conditions. Therefore, they should be useful probes for the investigation of PI and related biology, such as PI phosphorylation, PI interaction with other molecules in cells, and the functions of lipid structures in these processes.

Merging Worklife Organizational Innovation and Educational Programs: Promoting Continuous Adaptations to the Global Economy

AbstractThis essay honors the accomplishments of Morten Levin both by analyzing some of the key innovative educational programs he created and by extending the elements of that analysis to two other cases, that of Aker Solutions and the Mondragon Cooperatives. We acknowledge that the relationship between higher education institutions and manufacturing-service-public sector organizations is multiplex and has undergone multiple transformations over the decades. It is also affected by the national political economy and the scope and diversity of the higher education sector itself. Creating sustained and yet dynamic relations among these actors in service of both higher education and the efficacy and innovativeness of worklife organizations is a significant challenge which all the cases we present have addressed with some success. What the efforts have in common is a democratizing action research built on an ethos and worldview of the benefits of collaboration and co-creation, and organizational capabilities built on Actor Network strategies to promote collaboration and innovation as a way of adapting to the rapidly changing world conditions effectively. We argue that there are many more such examples in existence and that a key role for action research is to document them, show how more collaborative and adaptive organizational processes are possible and show how neoliberal New Public Management is an authoritarian “wrong turn” with dire consequences. Ultimately, this essay is an argument against the “iron cage” view of bureaucracy as always coercive and stultifying and show that it is not bureaucracy but the authoritarian approaches to creating coercive bureaucracies that destroy organizational collaborations and innovation.

Investigation of Bird Sound Transformer Modeling and Recognition

Birds play a pivotal role in ecosystem and biodiversity research, and accurate bird identification contributes to the monitoring of biodiversity, understanding of ecosystem functionality, and development of effective conservation strategies. Current methods for bird sound recognition often involve processing bird songs into various acoustic features or fusion features for identification, which can result in information loss and complicate the recognition process. At the same time, the recognition method based on raw bird audio has not received widespread attention. Therefore, this study proposes a bird sound recognition method that utilizes multiple one-dimensional convolutional neural networks to directly learn feature representations from raw audio data, simplifying the feature extraction process. We also apply positional embedding convolution and multiple Transformer modules to enhance feature processing and improve accuracy. Additionally, we introduce a trainable weight array to control the importance of each Transformer module for better generalization of the model. Experimental results demonstrate our model’s effectiveness, with an accuracy rate of 99.58% for the public dataset Birds_data, as well as 98.77% for the Birdsonund1 dataset, and 99.03% for the UrbanSound8K environment sound dataset.

Illuminating tandem reactions characterized by temporal separation of catalytic activities via DFT calculations: A case study of Ni-catalyzed alkyne semi-hydrogenation

The concept of “temporal separation of catalytic activities” outlines a scenario where multiple transformations within a catalytic tandem reaction proceed sequentially over time without mutual interference. After presenting several examples of such reactions, we specifically focus on an example of the Ni-catalyzed alkyne semi-hydrogenation as a significant case study. By performing density functional theory (DFT) calculations, we illuminate the unique dynamic character of the reaction that the intermediate remains dormant until the reactant exhausted. The insights gained from the present calculations have led us to propose a comprehensive energy landscape model for the catalytic tandem reactions with temporal separation of catalytic activities, which offers a logical explanation for the temporal dormancy of the intermediate. This class of reactions is expected to be highly valuable as it presents the opportunity to fine-tune individual reaction steps, thereby introducing fresh concepts for precise control of reactions in one-pot chemistry.

A comparison of deep learning models for proton background rejection with the AMS electromagnetic calorimeter

Abstract The alpha magnetic spectrometer (AMS) is a high-precision particle detector onboard the International Space Station containing six different subdetectors. The transition radiation detector and electromagnetic calorimeter (ECAL) are used to separate electrons/positrons from the abundant cosmic-ray proton background. The positron flux measured in space by AMS falls with a power law which unexpectedly softens above 25 GeV and then hardens above 280 GeV. Several theoretical models try to explain these phenomena, and a more accurate measurement of positrons at higher energies is needed to help test them. The currently used methods to reject the proton background at high energies involve extrapolating shower features from the ECAL to use as inputs for boosted decision tree and likelihood classifiers. We present a new approach for particle identification with the AMS ECAL using deep learning (DL). By taking the energy deposition within all the ECAL cells as an input and treating them as pixels in an image-like format, we train an MLP, a CNN, and multiple ResNets and convolutional vision transformers (CvTs) as shower classifiers. Proton rejection performance is evaluated using Monte Carlo (MC) events and ISS data separately. For MC, using events with a reconstructed energy between 0.2–2 TeV, at 90% electron accuracy, the proton rejection power of our CvT model is more than five times that of the other DL models. Similarly, for ISS data with a reconstructed energy between 50–70 GeV, the proton rejection power of our CvT model is more than 2.5 times that of the other DL models.

Error analysis and accuracy evaluation method for coordinate measurement in transformed coordinate system

Multiple coordinate system transformations are required for engineering measurements of multi-faceted components. Traditional engineering measurements neglect coordinate errors after transformations, leading to inaccurate accuracy evaluations for coordinates. In this study, the effects of angular deviation and coordinate value magnitude on coordinate errors during transformations are first analyzed. Additionally, the transformation deviation of the three-point method is examined. Subsequently, an accuracy evaluation method for coordinate measurements is proposed to determine the scale scaling factor and the formula for calculating the variance of measuring coordinate errors. Finally, a coordinate accuracy evaluation method and evaluation formula that consider coordinate system transformation errors are proposed by self-checking. The validity of the proposed accuracy evaluation formula is confirmed through experiments. The coordinate measurement accuracy of the low-cost binocular camera is higher than that of the total station, whether or not coordinate system transformations are considered. Stereo-vision measurement technology is more suitable for small-scale engineering measurements.

Automated Poisson regression exposure-response analysis for binary outcomes with PoissonERM.

PoissonERM is an R package used to conduct exposure-response (ER) analysis on binary outcomes for establishing the relationship between exposure and the occurrence of adverse events (AE). While Poisson regression could be implemented with glm(), PoissonERM provides a simple way to semi-automate the entire analysis and generate an abbreviated report as an R markdown (Rmd) file that includes the essential analysis details with brief conclusions. PoissonERM processes the provided data set using the information from the user's control script and generates summary tables/figures for the exposure metrics, covariates, and event counts of each endpoint (each type of AE). After checking the incidence rate of each AE, the correlation, and missing values in each covariate, an exposure-response model is developed for each endpoint based on the provided specifications. PoissonERM has the flexibility to incorporate and compare multiple scale transformations in its modeling. The best exposure metric is selected based on a univariate model's p-value or deviance ( ) as specified. If a covariate search is specified in the control script, the final model is developed using backward elimination. PoissonERM identifies and avoids highly correlated covariates in the final model development of each endpoint. Predicting event incidence rates using external (simulated) exposure metric data is an additional functionality in PoissonERM, which is useful to understand the event occurrence associated with certain dose regimens. The summary outputs of the cleaned data, model developments, and predictions are saved in the working folder and can be compiled into a HTML report using Rmd.

Microstructure of Gilsocarbon graphite revealed by a correlative study of optical texture and FIB-TEM

Transmission Electron Microscopy (TEM) alongside Polarized Optical Microscopy (POM), is used to investigate the microstructure of nuclear graphite, specifically Gilsocarbon coke, fillers, binders, and flour. Detailed analyses revealed domain structures, domain boundaries, sub-domains and two types of microcracks: intra-domain and inter-domain cracks. Analysis of TEM images, from samples prepared via Focused Ion Beam (FIB), showed domain sizes in coke filler and flour regions ranged from 1 to 3 μm, whereas binder regions had larger domains of up to tens of micrometers, with correspondingly longer Mrozowski cracks. Within these domains, sub-domain boundaries further constrained Mrozowski cracks, resulting in crack lengths significantly shorter than the domain size. Domain boundaries were classified into small-angle and large-angle boundaries based on orientation differences, with large-angle boundaries arising from multiple small-angle transformations facilitated by polygonal microcracks. This microstructural data obtained from virgin Gilsocarbon graphite provides essential inputs for an experimentally informed model predicting the deformation and fracture properties of this material at the micrometer length scale, which may offer improved insights to enhance our understanding of how these properties may evolve under reactor operating conditions.

Estimating Cadmium Concentration in Agricultural Soils with ZY1-02D Hyperspectral Data: A Comparative Analysis of Spectral Transformations and Machine Learning Models

The accumulation of cadmium (Cd) in agricultural soils presents a significant threat to crop safety, emphasizing the critical necessity for effective monitoring and management of soil Cd levels. Despite technological advancements, accurately monitoring soil Cd concentrations using satellite hyperspectral technology remains challenging, particularly in efficiently extracting spectral information. In this study, a total of 304 soil samples were collected from agricultural soils surrounding a tungsten mine located in the Xiancha River basin, Jiangxi Province, Southern China. Leveraging hyperspectral data from the ZY1-02D satellite, this research developed a comprehensive framework that evaluates the predictive accuracy of nine spectral transformations across four modeling approaches to estimate soil Cd concentrations. The spectral transformation methods included four logarithmic and reciprocal transformations, two derivative transformations, and three baseline correction and normalization transformations. The four models utilized for predicting soil Cd were partial least squares regression (PLSR), support vector machine (SVM), bidirectional recurrent neural networks (BRNN), and random forest (RF). The results indicated that these spectral transformations markedly enhanced the absorption and reflection features of the spectral curves, accentuating key peaks and troughs. Compared to the original spectral curves, the correlation analysis between the transformed spectra and soil Cd content showed a notable improvement, particularly with derivative transformations. The combination of the first derivative (FD) transformation with the RF model yielded the highest accuracy (R2 = 0.61, RMSE = 0.37 mg/kg, MAE = 0.21 mg/kg). Furthermore, the RF model in multiple spectral transformations exhibited higher suitability for modeling soil Cd content compared to other models. Overall, this research highlights the substantial applicative potential of the ZY1-02D satellite hyperspectral data for detecting soil heavy metals and provides a framework that integrates optimal spectral transformations and modeling techniques to estimate soil Cd contents.

Structure Induced by a Multiple Membership Transformation on the Conditional Autoregressive Model

The objective of disease mapping is to model data aggregated at the areal level. In some contexts, however, (e.g. residential histories, general practitioner catchment areas) when data is arising from a variety of sources, not necessarily at the same spatial scale, it is possible to specify spatial random effects, or covariate effects, at the areal level, by using a multiple membership principle (MM) (Petrof et al., 2020; Gramatica et al., 2021). A weighted average of conditional autoregressive (CAR) spatial random effects embeds spatial information for a spatially-misaligned outcome and estimate relative risk for both frameworks (areas and memberships). In this paper we investigate the theoretical underpinnings of these application of the multiple membership principle to the CAR prior, in particular with regard to parameterisation, properness and identifiability. We carry out simulations involving different numbers of memberships as compared to number of areas and assess impact of this on estimating parameters of interest. Both analytical and simulation study results show under which conditions parameters of interest are identifiable, so that we can offer actionable recommendations to researchers. Finally, we present the results of an application of the multiple membership model to diabetes prevalence data in South London, together with strategic implications for public health considerations.

Viscosity of deep eutectic solvents: Predictive modeling with experimental validation

Viscosity, the measure of a fluid's resistance to deformation, is a critical parameter in many industries. Being able to accurately predict viscosity is essential for the successful design and optimization of technological processes. In this research, regression models were created to predict the viscosity of deep eutectic solvents (DESs). Machine learning models were trained using a data set of 3440 data points for two component DESs. Different algorithms, such as Multiple Linear Regression, Random Forest, CatBoost, and Transformer CNF, were employed alongside a variety of structural representations like fingerprints, σ-profiles, and molecular descriptors. The effectiveness of the models was assessed for interpolation tasks within the training data and extrapolation outside of it. The results indicate that a rigorous splitting of the dataset into subsets is necessary to accurately evaluate the performance of the models. Two new choline chloride-based DESs were prepared and their viscosities were measured to evaluate the predictive capabilities of the models. The CatBoost algorithm with CDK molecular descriptors was chosen as the recommended model. The average absolute relative deviations (AARD) of this model exhibited fluctuations during 5-fold cross-validation, ranging from 10.8 % when interpolating within the dataset to 88 % when extrapolating to new mixture components. The open access model was presented in this study (http://chem-predictor.isc-ras.ru/ionic/des/).

On Laplace invariants of two-dimensional nonlinear equations of the second order with homogeneous polynomial

We study two-dimensional nonlinear partial differential equations of the second order with variable coefficients. The left-hand side of these equations is a homogeneous polynomial of the second degree in unknown function and its derivatives. We consider a set of linear multiplicative transformations of the unknown function which keep the form of the initial equation. By analogy with linear equations, the Laplace invariants are determined as the invariants of this transformation. The expressions for the Laplace invariants in terms of the coefficients of the equation and their first derivatives are obtained. For the considered equations, we found the equivalent systems of the first order equations containing the Laplace invariants. It is shown that if one of the Laplace invariants equals zero, the corresponding system is reduced to one equation of the first order. Also in this case, the solution of the initial equation can be obtained in quadratures if some additional conditions on the coefficients are met. The investigations are executed for a hyperbolic equation with a mixed derivative and for a nonlinear second order equation of the general form with a homogeneous polynomial of the second degree in unknown function and its derivatives. We obtained for these cases the Laplace invariants and equivalent systems of the first order equations.

Heterologous Expression of Sunflower HaHPT and HaTMT Genes Enhances Rice-Grain Vitamin E Content.

Insufficient dietary vitamin intake can lead to severe health conditions in humans. Improving the vitamin E (VE) content of food crops such as rice through breeding is an economical and effective means to alleviate this problem. In this study, Homogentisate phytyltransferase (HPT) and γ-tocopherol methyltransferase (γ-TMT), two genes derived from sunflower (Helianthus annuus L., a high VE species), were introduced into an elite rice (Oryza sativa L.) cultivar "Ningjing 7" for biofortification. We verified the successful expression of the two genes in multiple transformation events. High-performance liquid chromatography revealed that transgenic plants expressing either HaHPT alone or HaHPT and HaTMT accumulate more VE compared with the wild type. We also revealed that the level of α-tocopherol, the form of VE with the highest biological activity, had increased to 2.33 times in transgenic HaTMT plants compared with the wild type. Transcriptome analysis revealed that the expression levels of some chlorophyll synthesis pathway genes related to VE precursor synthesis significantly increased during grain filling in transgenic rice grains. No difference in agronomic traits was observed between the transgenic plants and their wild type except for a slightly reduced plant height associated with the transgenic plants. These data demonstrate that the heterologous expression of HaHPT gene is effective in increasing the total VE content, while HaTMT plays an important role in the relative abundance of α-tocopherol in rice grains. This study demonstrates a promising strategy for breeding rice with elevated VE content via metabolic engineering.

Correlation of austenite grain size with ε-/α′-martensitic transformation and mechanical properties in a high-manganese damping steel

One of the core studies on low stacking fault energy (SFE) high-manganese steel is to maintain excellent damping capacity while achieving high strength and toughness. The austenite grain size (AGS) in high-Mn steel affects its SFE and even the strength-plasticity and damping capacity of the steel, but the detailed mechanism related to martensitic transformation has not been clarified. In this study, Fe-17Mn high-Mn damping steel is used as the material to obtain austenite grains of different sizes by controlling the cold rolling and annealing processes. By using in-situ tensile testing, damping capacity testing, and various microscopic analytical techniques, the influence of AGS on the thermal/deformation induced martensitic transformation, work hardening behavior, mechanical properties and damping capacity of the steel is mainly studied. The results indicate that the refinement of austenite grain increases the SFE of high-Mn damping steel and reduces the amount of thermal induced ε-martensite (εth) and stacking faults (SFs). The deformation induced martensitic transformations (DIMTs) in high-Mn damping steel during tensile deformation include γ→εD, εth→α′ and εD→α′ transformation. The deformation induced ε-martensitic transformation is more helpful to improve the work hardening rate than deformation induced α′-martensitic transformation. Due to the concentration of strain partitioning at grain boundaries, twin boundaries and phase boundaries, refinement of austenite grain can promote deformation induced ε-martensitic transformation and improve the steel strength. However, excessively small austenite grains can suppress deformation induced α′-martensitic transformation leading to a significant decrease in elongation. Under the synergetic effects of grain refinement strengthening and multiple martensitic transformations, when the average AGS is about 4.0 μm, the steel has high strength and high plasticity, with a tensile strength of 919 MPa, a yield strength of 518 MPa and an elongation of 46.4 %. The product of tensile strength and elongation is up to 42.6 GPa·%. At low or high strain amplitudes, the damping capacity of this steel shows an opposite trend with the refinement of AGS. The damping capacity of the steel gradually increases with grain refinement at low strain amplitudes of 0.01 %–0.065 %, mainly due to the reduction of the content of weak pinning points. At high strain amplitudes of 0.065 %–0.1 %, the damping capacity of the steel gradually decreases for the reduction of damping sources with the refinement of grain size.

Enhanced EEG seizure recognition after hypoxia-ischemia in fetal sheep using transformer-based wavelet-scalogram deep learning

This study introduces transformer-based deep learning models as superior alternatives to convolutional neural network (CNN) architectures for seizure detection after hypoxia–ischemia (HI) in electroencephalography (EEG) recordings in the developing brain. We further demonstrate that these models excel in classifying of varied and subtle morphologies of electrographic seizures observed in the EEG of fetal sheep after HI. The study explores model training combinations from four cohorts of Romney/Suffolk fetal sheep (HI-normothermia-term (n = 7), HI-hypothermia-term (n = 14), sham-normothermia-term (n = 5), and HI-normothermia-preterm (n = 14)), totalling 31,015 EEG segments from 17,300 h of recordings. We evaluated multiple Transformer architectures using both 2D wavelet-scalograms (WS) and 1D raw EEG signals through leave-one-out and k-fold cross-validation, comparing models’ performance with or without sham-normothermia-term data and contrasting them with CNN-based results from our previous work. The Data-Efficient image Transformer (DeiT) emerged as the top-performing model, achieving 99.60 % accuracy (AUC = 0.992) when fed with WS, surpassing the 2D WS CNN result (98.94 % accuracy, AUC = 0.967) from our prior study. Notably, the Wav2Vec2 transformer model, fed with raw 1D EEG segments, demonstrated comparable performance with 99.60 % accuracy (AUC = 0.992), outperforming the 1D CNN result (98.43 % accuracy, AUC = 0.961) from the previous study. More importantly, the transformers can adeptly distinguish between seizures in both the preterm and term brain, and under the influence of treatment with 99.92 % accuracy (AUC = 0.997). Our findings highlight the superior ability of transformer models to capture long-range dependencies within EEG data, enhancing seizure detection without the added complexity of generating 2D WS images. Transformer models show promise for generalized automated seizure detection, irrespective of the perinatal brain maturation stage and the influence of hypothermia, offering potential improvements for seizure detection after HI in newborns.

Nitrogen fertilization and soil nitrogen cycling: Unraveling the links among multiple environmental factors, functional genes, and transformation rates

Anthropogenic nitrogen (N) inputs substantially influence the N cycle in agricultural ecosystems. However, the potential links among various environmental factors, nitrogen functional genes, and transformation rates under N fertilization remain poorly understood. Here, we conducted a five-year field experiment and collected 54 soil samples from three 0–4 m boreholes across different treatments: control, N-addition (nitrogen fertilizer) and NPK-addition (combined application of nitrogen, phosphorus and potassium fertilizers) treatments. Our results revealed pronounced variations in soil physiochemical parameters, metal concentrations and antibiotic levels under both N and NPK treatments. These alternations induced significant shifts in bacterial and fungal communities, altered NFG abundance and composition, and greatly enhanced rates of nitrate reduction processes. Notably, nutrients, antibiotics and bacteria exerted a more pronounced influence on NFGs and nitrate reduction under N treatment, whereas nutrients, metals, bacteria and fungi had a significant impact under NPK treatment. Furthermore, we established multidimensional correlations between nitrate reduction gene profiles and the activity rates under N and NPK treatments, contrasting with the absence of significant relationships in the control treatment. These findings shed light on the intricate relationships between microbial genetics and ecosystem functions in agricultural ecosystem, which is of significance for predicting and managing metabolic processes effectively.

Multiple Structural and Phase Transformations of MOF and Selective Hydrocarbon Gas Separation in its Amorphous, Glass Phase States.

The first wide-view image of multiple structural and phase transformations for MOFs from crystal state transformations further to the extreme limit approaching liquid/glass phase, was presented based on a square-layer framework of [Co2(pybz)2(CH3COO)2]·DMF (Co2). The process involves i) an initial crystalline transformation brings to a 3-fold interpenetrated and ordered vacancies contained framework [Co(pybz)2(CH3OH)2]·2CH3OH (CoM) due to in-situ disassemble-reassemble, ii) thermal induced departure of a pair of cis-form coordinated methanol in CoM leads to amorphous framework (a-dCoM), iii) glass transition (Tg = 566 K) to super-cooled liquid (scl-dCoM, spanning 38 K), iv) obtaining MOF glass g-dCoM upon quenching the super-cooled liquid, and v) re-crystallization of super-cooled liquid to six-fold interpenetrated dia-net framework [Co(pybz)2]6n (rec-dCoM) under heating above 604 K. The access to glass from CoM, provides a new self-perturbation strategy to create more MOF glasses without melting. The wider pore size distribution in amorphous/glassy MOFs than crystalline precursor realized the first time selective hydrocarbon gas separation by breakthrough experiments, which bring efficient separation of 1:99 C2H2/C2H4 by either a-dCoM or g-dCoM and produce polymer grade C2H4 with purity ≥ 99.5% after a single adsorption process. Furthermore, the mixture of 50:50 C3H6/C3H8 can be separated by a-dCoM.

Blue luminescent pyridine-tethered quinazoline framework: Highly selective and sensitive picric acid chemosensor

The microwave-assisted one-pot three-component reaction of 2-phenyl-7,8-dihydroquinazolin-5(6H)-one, 2-arylidenemalononitrile and ammonium acetate under solvent-free conditions occurred through a five-step multiple bond-forming transformation to afford 2-amino-8-phenyl-4-aryl-5,6-dihydropyrido[2,3-f]quinazoline-3-carbonitriles in excellent yields. All the quinazoline-3-carbonitriles exhibited bright blue fluorescence under a UV lamp and were found to be efficient chemosensors of picric acid with detection limits in the nano-molar range.