Research on the Performance of Cement-Based Composite Borehole Sealing Material Based on Orthogonal Test.

In order to achieve better sealing of boreholes, the performance of sealing materials is modified to improve the efficiency of coalbed methane extraction. In this paper, a new type of cement-based hole sealing material was prepared by using silicate cement (PC) and cement sulfoaluminate (SAC) as raw materials, supplemented with various additives, such as fly ash, Na2SO4, Ca(OH)2, and poly(vinyl alcohol) (PVA) fiber. The effects of these additives on the fluidity, setting time, and compressive strength of the PC-SAC compounded cementitious pore sealing material were investigated by orthogonal tests, and the hydration process and hydration products were analyzed by X-ray diffraction (XRD), thermogravimetry-differential thermogravimetry (TG-DTG), and scanning electron microscopy (SEM). The results show that the water-cement ratio has the most significant influence on the various properties of the material; the two additives of Na2SO4 and Ca(OH)2 play a key role in the setting time of the material; the optimal group, i.e., water-cement ratio of 0.5, fly ash of 5%, Na2SO4 of 1%, Ca(OH)2 of 0.75%, and PVA fibers of 0.8%, is obtained by the orthogonal test method, which is the closest to the actual needs of the project. The hydration products of the optimized materials have obvious changes, and the needle-like AFt and C-S-H increase so that the performance of the materials has been significantly improved.

Printed Divisional Optical Biochip for Multiplex Visualizable Exosome Analysis at Point-of-Care.

Rapid detection of various exosomes is of great significance in early diagnosis and postoperative monitoring of cancers. Here, a divisional optical biochip is reported for multiplex exosome analysis via combining the self-assembly of nanochains and precise surface patterning. Arising from resonance-induced near-field enhancement, the nanochains show distinct color changes after capturing target exosomes for direct visual detection. Then, a series of divisional nanochain-based biochips conjugated with several specific antibodies are fabricated through designed hydrophilic and hydrophobic patterns. Because of the significant wettability difference, one sample droplet is precisely self-splitting into several microdroplets enabling simultaneous identification of multiple target exosomes in 30 min with a sensitivity of 6 × 107 particles mL-1 , which is about two orders lower than enzyme-linked immunosorbent assay. Apart from the trace amount detection, excellent semiquantitative capability is demonstrated to distinguish clinical exosomes from glioblastoma patients and healthy people. This method is simple, versatile, and highly efficient that can be extended as a diagnostic tool for many diseases, promoting the development of liquid biopsy.

Perovskite-based photodetector for real-time and quantitative monitoring of sports motion.

Reliable monitoring the movement amplitude and dynamics during sports exercise is significant for improving training results and preventing training wound. Here, we present a printed perovskite-based photodetector for real-time and quantitative monitoring of sports motion. The ordered nucleation and growth of perovskite crystals are regulated by the 4-acetamidothiophenol (AMTP) at the interface, which promotes the size of perovskite crystals into the micrometer. Benefiting from the uniformity of the AMTP-regulated MAPbI3, the as-prepared photodetector gives great photocurrent response under indoor light or outdoor light. During the exercise, real-time monitoring sports motion is achieved through detecting the illumination changing of photodetectors attaching on the wrist and ankles. Moreover, twelve kinds of common sports can be quantitatively analyzed with the detection of illumination changing on the photodetector. Such photodetector provides an efficient measurement method of wearable electronics for sports monitoring.

Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm.

As a principal energy globally, coal's quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific industrial requirements due to their unique attributes. Traditional methods for coal classification, typically relying on manual examination and chemical assays, lack efficiency and fail to offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based on the reflectance spectrum of coal and machine learning. This method approach facilitates the rapid and accurate classification of coal types through the analysis of coal spectral data. First, the reflection spectra of three types of coal, namely, bituminous coal, anthracite, and lignite, were collected and preprocessed. Second, a model utilizing two hidden layer extreme learning machine (TELM) and affine transformation function is introduced, which is called affine transformation function TELM (AT-TELM). AT-TELM introduces an affine transformation function on the basis of TELM, so that the hidden layer output satisfies the maximum entropy principle and improves the recognition performance of the model. Third, we improve AT-TELM by optimizing the weight matrix and bias of AT-TELM to address the issue of highly skewed distribution caused by randomly assigned weights and biases. The method is named the improved affine transformation function (IAT-TELM). The experimental findings demonstrate that IAT-TELM achieves a remarkable coal classification accuracy of 97.8%, offering a cost-effective, rapid, and precise method for coal classification.

Carbon budget of different forests in China estimated by an individual-based model and remote sensing.

Forests play a key role in the regional or global carbon cycle. Determining the forest carbon budget is of great significance for estimating regional carbon budgets and formulating forest management policies to cope with climate change. However, the carbon budget of Chinese different forests and their relative contributions are not completely clear so far. We evaluated the carbon budget of different forests from 1981 to 2020 in China through combining model with remote sensing observation. In addition, we also determined the relative contribution of carbon budget of each forest type to all forests in China. Eight forest types were studied: evergreen coniferous forest (ECF), deciduous coniferous forest (DCF), coniferous and broad-leaved mixed forest (CBF), deciduous broad-leaved forest (DBF), evergreen broad-leaved forest (EBF), evergreen deciduous broad-leaved mixed forest (EDBF), seasonal rain forest (SRF), and rain forest (RF). The results indicated that the Chinese forests were mainly carbon sink from 1981 to 2020, particularly the annual average carbon budget of forest from 2011 to 2020 was 0.191 PgC·a-1. Spatially, the forests' carbon budget demonstrated obvious regional differences, gradually decreasing from Southeast China to Northwest China. The relative contributions of carbon budget in different forests to all forests in China were different. During 2011-2020, the ECF forests contributed the most carbon budget (34.45%), followed by DBF forests (25.89%), EBF forests (24.82%), EDBF forests (13.10%), RF forests (2.23%), SRF forests (3.14%) and CBF forests (1.14%). However, the DCF forests were found mainly as carbon source. These results contribute to our understanding of regional carbon budget of forests.

Molecular Recognition-Modulated Hetero-Assembly of Nanostructures for Visualizable and Portable Detection of Circulating miRNAs.

Biomolecular markers, particularly circulating microRNAs (miRNAs) play an important role in diagnosis, monitoring, and therapeutic intervention of cancers. However, existing detection strategies remain intricate, laborious, and far from being developed for point-of-care testing. Here, we report a portable colorimetric sensor that utilizes the hetero-assembly of nanostructures driven by base pairing and recognition for direct detection of miRNAs. Following hybridization, two sizes of nanoparticles modified with single-strand DNA can be robustly assembled into heterostructures with strong optical resonance, exhibiting distinct structure colors. Particularly, the large nanoparticles are first arranged into nanochains to enhance scattering signals of small nanoparticles, which allows for sensitive detection and quantification of miRNAs without the requirement of target extraction, amplification, and fluorescent labels. Furthermore, we demonstrate the high specificity and single-base selectivity of testing different miRNA samples, which shows great potential in the diagnosis, staging, and monitoring of cancers. These heterogeneous assembled nanostructures provide an opportunity to develop simple, fast, and convenient tools for miRNAs detection, which is suitable for many scenarios, especially in low-resource setting.

Rapid detection of molybdenum ore grade based on visible-infrared spectroscopy and MTSVD-TGJO-ELM.

The rapid determination of ore grade can improve the efficiency of beneficiation. The existing molybdenum ore grade determination methods lag behind the beneficiation work. Therefore, this paper proposes a method based on a combination of Visible-infrared spectroscopy and machine learning to rapidly determine molybdenum ore grade. Firstly, 128 molybdenum ores were collected as spectral test samples to obtain spectral data. Then 13 latent variables were extracted from the 973 spectral features using partial least square. The Durbin-Watson test and the runs test were used to detect the partial residual plots and augmented partial residual plots of LV1 and LV2 to determine the non-linear relationship between spectral signal and molybdenum content. Extreme Learning Machine (ELM) was used instead of linear modeling methods to model the grade of molybdenum ores because of the non-linear behavior of the spectral data. In this paper, the Golden Jackal Optimization of adaptive T-distribution was used to optimize the parameters of the ELM to solve the problem of unreasonable parameters. Aiming at solving ill-posed problems by ELM, this paper decomposes the ELM output matrix by using the improved truncated singular value decomposition. Finally, this paper proposes an extreme learning machine method based on a modified truncated singular value decomposition and a Golden Jackal Optimization of adaptive T-distribution (MTSVD-TGJO-ELM). Compared with other classical machine learning algorithms, MTSVD-TGJO-ELM has the highest accuracy. This provides a new method for rapid detection of ore grade in the mining process and facilitates accurate beneficiation of molybdenum ores to improve ore recovery rate.

Wearable perovskite solar cells by aligned liquid crystal elastomers.

In a flexible perovskite solar cell, the bottom interface between perovskite and the electron-transporting layer is critical in determining its efficiency and reliability. High defect concentrations and crystalline film fracturing at the bottom interface substantially reduce the efficiency and operational stability. In this work, a liquid crystal elastomer interlayer is intercalated into a flexible device with the charge transfer channel toughened by the aligned mesogenic assembly. The molecular ordering is instantly locked upon photopolymerization of liquid crystalline diacrylate monomers and dithiol-terminated oligomers. The optimized charge collection and the minimized charge recombination at the interface boost the efficiency up to 23.26% and 22.10% for rigid and flexible devices, respectively. The liquid crystal elastomer-induced suppression of phase segregation endows the unencapsulated device maintaining >80% of the initial efficiency for 1570 h. Moreover, the aligned elastomer interlayer preserves the configuration integrity with remarkable repeatability and mechanical robustness, which enables the flexible device to retain 86% of its original efficiency after 5000 bending cycles. The flexible solar cell chips are further integrated into a wearable haptic device with microneedle-based arrays of sensors to demonstrate a pain sensation system in virtual reality.

Fast and Sensitive Detection of Protein Markers Using an All-Printing Photonic Crystal Microarray via Fingertip Blood.

With the demand for point-of-care testing (POCT) in cardiovascular diseases, the detection of biomarkers in trace blood samples is of great significance in emergency medicine settings. Here, we demonstrated an all-printed photonic crystal microarray for POCT of protein markers (named "P4 microarray"). The paired nanobodies were printed as probes to target the soluble suppression of tumorigenicity 2 (sST2) as a certified cardiovascular protein marker. Benefiting from photonic crystal-enhanced fluorescence and integrated microarrays, quantitative detection of sST2 is 2 orders of magnitude lower than that of a traditional fluorescent immunoassay. The limit of detection is down to 10 pg/mL with the coefficient of variation being less than 8%. Detection of sST2 via fingertip blood is achieved in 10 min. Moreover, the P4 microarray after 180 days of storage at room temperature showed excellent stability for detection. This P4 microarray, as a convenient and reliable immunoassay for rapid and quantitative detection of protein markers in trace blood samples, has high sensitivity and strong storage stability, which hold great potential to advance cardiovascular precision medicine.

Rapid Identification and Monitoring of Multiple Bacterial Infections Using Printed Nanoarrays.

Fast and accurate detection of microbial cells in clinical samples is highly valuable but remains a challenge. Here, a simple, culture-free diagnostic system is developed for direct detection of pathogenic bacteria in water, urine, and serum samples using an optical colorimetric biosensor. It consists of printed nanoarrays chemically conjugated with specific antibodies that exhibits distinct color changes after capturing target pathogens. By utilizing the internal capillarity inside an evaporating droplet, target preconcentration is achieved within a few minutes to enable rapid identification and more efficient detection of bacterial pathogens. More importantly, the scattering signals of bacteria are significantly amplified by the nanoarrays due to strong near-field localization, which supports a visualizable analysis of the growth, reproduction, and cell activity of bacteria at the single-cell level. Finally, in addition to high selectivity, this nanoarray-based biosensor is also capable of accurate quantification and continuous monitoring of bacterial load on food over a broad linear range, with a detection limit of 10 CFU mL-1 . This work provides an accessible and user-friendly tool for point-of-care testing of pathogens in many clinical and environmental applications, and possibly enables a breakthrough in early prevention and treatment.

Lateral Heterostructured Vis-NIR Photodetectors with Multimodal Detection for Rapid and Precise Classification of Glioma.

Precise diagnosis of the boundary and grade of tumors is especially important for surgical dissection. Recently, visible and near-infrared (Vis-NIR) absorption differences of tumors are demonstrated for a precise tumor diagnosis. Here, a template-assisted sequential printing strategy is investigated to construct lateral heterostructured Vis-NIR photodetectors, relying on the up-conversion nanoparticles (UCNPs)/perovskite arrays. Under the sequential printing process, the synergistic effect and co-confinement are demonstrated to induce the UCNPs to cover both sides of the perovskite microwire. The side-wrapped lateral heterogeneous UCNPs/perovskite structure exhibits more satisfactory responsiveness to Vis-NIR light than the common fully wrapped structure, due to sufficient visible-light-harvesting ability. The Vis-NIR photodetectors with R reaching 150 mA W-1 at 980 nm and 1084 A W-1 at 450 nm are employed for the rapid classification of glioma. The detection accuracy rate of 99.3% is achieved through a multimodal analysis covering the Vis-NIR light, which provides a reliable basis for glioma grade diagnosis. This work provides a concrete example for the application of photodetectors in tumor detection and surgical diagnosis.

Printing nanoparticle-based isotropic/anisotropic networks for directional electrical circuits.

With the demand for integrated nanodevices, anisotropic conductive films are one type of interconnection structure for electronic components, which have been widely used for improving the integration of the system in printed circuit boards. This work presents a template-assisted printing strategy for the fabrication of nanoparticle-based networks with multi electrical properties. By manipulating the microfluid behavior under the guidance of the grid-shaped template, the continuity of liquid bridges can be precisely controlled in two directions. The isotropous circuits with crossbar paths, discrete paths as well as unidirectional paths are obtained, which achieve the switching of on/off states in the circuits. This work demonstrates a new type of directional circuits by the template-assisted printing method, which provides an effective fabrication strategy for electrical components and integrated systems.

All-printed point-of-care immunosensing biochip for one drop blood diagnostics.

Designing and preparing a fast and easy-to-use immunosensing biochip are of great significance for clinical diagnosis and biomedical research. In particular, sensitive, specific, and early detection of biomarkers in trace samples promotes the application of point-of-care testing (POCT). Here, we demonstrate an all-printed immunosensing biochip with the characteristics of hydrodynamic enrichment and photonic crystal-enhanced fluorescence. Direct quantitative detection of cardiac biomarkers via one drop of blood is achieved in 10 min. After simulating the hydrodynamic behavior of one droplet serum on the printed assay, creatine kinase-MB (CK-MB) has been recognized and located on the photonic crystal arrays. Benefiting from the fluorescence enhancement effect, quantitative detection of CK-MB has been demonstrated from 0.01 ng ml-1 to 100 ng ml-1, which is superior to the conventional enzyme-linked immunosorbent assay (ELISA). This strategy provides a general and easy-to-use approach for fast quantitative detection of biomarkers, which would be improved further for portable clinical diagnostics and home medical monitoring.

An integrated remote sensing and model approach for assessing forest carbon fluxes in China.

Forest plays an important role in reducing pressure on the natural environment, weaking the influence of greenhouse effects, and sequestrating atmospheric carbon dioxide. So far, due to the lack of complete understanding of forest ecosystem processes and the limitations on the scope of application of evaluation methods, there are still great uncertainties in the researches on carbon fluxes of forest ecosystems in China at the national level. In this study, an individual tree species FORCCHN model, which could flexibly use the inventory data as the initial field (more accurately) or use the remote sensing information to inverse initial field was applied. The dynamics of key carbon cycle fluxes (net primary productivity (NPP) and net ecosystem productivity (NEP)) and carbon sequestration of forest ecosystems in China from 1982 to 2019 were simulated based on remote sensing data and FORCCHN model. The results showed that forest ecosystems in China had great carbon sequestration potential over the past 39 years. From 1982 to 2019, the NPP of Chinese forests presented a fluctuated increase. Total NPP from 2011 to 2019 ranged from 0.91 PgC·a-1 to 1.14 PgC·a-1. Annual average NEP of forest ecosystems in China from 2011 to 2019 was 0.199 PgC·a-1 (1Pg = 1015 g). Influenced by climate, soil and vegetation, carbon sequestration potential in Chinese forest ecosystems presented obvious regional differences in space. The spatial distribution of NEP gradually increased from Northwest to Southeast China. From 2011 to 2019, forests in Yunnan Province had the strongest carbon storage capacity (72.79 TgC·a-1, 1Tg = 1012 g), followed by forests in Guangxi (18.49 TgC·a-1) and forests in Guangdong (10.01 TgC·a-1). Our results not only address concerns about carbon sequestration but also reflect the importance of Chinese forest resources in the development of the national economy and society.

Study on the Strength and Micro Characteristics of Grouted Specimens with Different Superfine Cement Contents.

To provide the most effective comprehensive performance grouting material ratio, in this experimental investigation, a total of eight grouted specimens with two water-cement ratios (0.45:1, 0.55:1) and four different superfine cement contents (0%, 30%, 70%, 100%) were evaluated. Based on a uniaxial compression test, the fractal dimension of the fragments, a mercury injection test, and scanning electron microscopy, the effects of the superfine cement content on the strength characteristics and microscopic characteristics of the grouted specimens were studied. The results showed that increasing the superfine cement content could enhance the compressive and tensile strength of the grouted specimens and reduce the fractal dimension of the fragments and the porosity of the grouted specimens. The superfine cement content increased from 0% to 70% when the water-cement ratio was 0.45:1. The compressive strength of the grouted specimens increased from 16.7 MPa to 26.3 MPa, and the fractal dimension decreased from 1.8645 to 1.2301. When the water-cement ratio was 0.55:1, the compressive strength of the grouted specimens increased from 10.5 MPa to 20.6 MPa, and the fractal dimension value decreased from 2.2955 to 1.4458. When the superfine cement content increased from 0% to 100%, the water-cement ratio was 0.45:1. The porosity of the grouted specimens was reduced from 28.41% to 21.62%. When the water-cement ratio was 0.55:1, the porosity of the grouted specimens was reduced from 33.33% to 29.46%.

Long-term variations in solar radiation, diffuse radiation, and diffuse radiation fraction caused by aerosols in China during 1961-2016.

The effects of atmospheric aerosols on the terrestrial climate system are more regional than those of greenhouse gases, which are more global. Thus, it is necessary to examine the typical regional effects of how aerosols affect solar radiation in order to develop a more comprehensive understanding. In this study, we used global AErosol RObotic NETwork (AERONET) data and robust radiation observational evidence to investigate the impact of aerosols on total radiation, diffuse radiation, and the diffuse radiation fraction in China from 1961 to 2016. Our results showed that there were different temporal changes in the aerosol optical depth (AOD), total solar radiation, diffuse radiation and diffuse radiation fraction over the past 56 years. Specifically, the 550 nm AOD from 2005 to 2016 decreased significantly, with annual average AOD of 0.51. Meanwhile, the average total solar radiation reduced by 2.48%, while there was a slight increase in average diffuse radiation at a rate of 3.10 MJ·m-2·yr-1. Moreover, the spatial heterogeneities of AOD, total radiation, diffuse radiation, and the diffuse radiation fraction in China were significant. Aerosol particle emissions in the developed eastern and southern regions of China were more severe than those in the western regions, resulting in higher total radiation and diffuse radiation in the western plateau than in the eastern plain. In addition, aerosols were found to have negative effects on total radiation and sunshine hours, and positive effects on diffuse radiation and diffuse radiation fraction. Further, the diffuse radiation fraction was negatively correlated with sunshine hours. However, there was a positive correlation between AOD and sunshine hours. These results could be used to assess the impacts of climate change on terrestrial ecosystem productivity and carbon budgets.

Copper Content Inversion of Copper Ore Based on Reflectance Spectra and the VTELM Algorithm.

Copper is an important national resource, which is widely used in various sectors of the national economy. The traditional detection of copper content in copper ore has the disadvantages of being time-consuming and high cost. Due to the many drawbacks of traditional detection methods, this paper proposes a new method for detecting copper content in copper ore, that is, through the spectral information of copper ore content detection method. First of all, we use chemical methods to analyze the copper content in a batch of copper ores, and accurately obtain the copper content in those ores. Then we do spectrometric tests on this batch of copper ore, and get accurate spectral data of copper ore. Based on the data obtained, we propose a new two hidden layer extreme learning machine algorithm with variable hidden layer nodes and use the regularization standard to constrain the extreme learning machine. Finally, the prediction model of copper content in copper ore is established by using the algorithm. Experiments show that this method of detecting copper ore content using spectral information is completely feasible, and the algorithm proposed in this paper can detect the copper content in copper ores faster and more accurately.