A complete review on the oxygen-containing functional groups of biochar: Formation mechanisms, detection methods, engineering, and applications.

Biochar is a porous carbon material generated by the thermal treatment of biomass under anaerobic or anoxic conditions with wealthy Oxygen-containing functional groups (OCFGs). To date, OCFGs of biochar have been extensively studied for their significant utility in pollutant removal, catalysis, capacitive applications, etc. This review adopted a whole system philosophy and systematically summarizes up-to-date knowledge of formation, detection methods, engineering, and application for OCFGs. The formation mechanisms and detection methods of OCFGs, as well as the relationships between OCFGs and pyrolysis conditions (such as feedstocks, temperature, atmosphere, and heating rate), were discussed in detail. The review also summarized strategies and mechanisms for the oxidation of biochar to afford OCFGs, with the performances and mechanisms of OCFGs in the various application fields (environmental remediation, catalytic biorefinery, and electrode material) being highlighted. In the end, the future research direction of biochar OCFGs was put forward.

Diarylheptanoid glycosides from Dioscorea nipponica rhizomes.

A group of previously undescribed diarylheptanoids with mono/di-glucose substitution, diodiarylheptosides A-F (1-6), together with six known diarylheptanoids (7-12) were isolated from the rhizomes of Dioscorea nipponica. Their structures were established by comprehensive UV, IR, HR-ESI-MS and NMR analyses, and their absolute configurations were determined by a comparison of calculated and experimental ECD, some with optical rotations, after acid-hydrolysis. Moreover, bioassay results showed that compounds 3 and 11 exhibited stronger NO inhibitions on lipopolysaccharides-induced RAW 264.7 cells, with the IC50 values of 14.91 ± 0.62 and 12.78 ± 1.12 µM.

Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass.

Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine learning (AutoML) approach, automatically training without human intervention, was used to aid in predicting gaseous production and interpreting the formation mechanisms of four gases (CO2, CH4, CO, and H2). Specifically, four accurate optimal single-target models based on AutoML were developed with elemental compositions and HTL conditions as inputs for four gases. Herein, the gradient boosting machine (GBM) performed excellently with train R2 ≥ 0.99 and test R2 ≥ 0.80. Then, the screened GBM algorithm-based ML multi-target models (maximum average test R2 = 0.89 and RMSE = 0.39) were built to predict four gases simultaneously. Results indicated that biomass carbon, solid content, pressure, and biomass hydrogen were the top four factors for gas production from HTL of biomass. This study proposed an AutoML-aided prediction and interpretation framework, which could provide new insight for rapid prediction and revelation of gaseous compositions from the HTL process.

Predicting co-liquefaction bio-oil of sewage sludge and algal biomass via machine learning with experimental optimization: Focus on yield, nitrogen content, and energy recovery rate.

Machine learning (ML), a powerful artificial intelligence tool, can effectively assist and guide the production of bio-oil from hydrothermal liquefaction (HTL) of wet biomass. However, for hydrothermal co-liquefaction (co-HTL), there is a considerable lack of application of experimentally verified ML. In this work, two representative wet biomasses, sewage sludge and algal biomass, were selected for co-HTL. The Gradient Boosting Regression (GBR) and Random Forest (RF) algorithms were employed for regression and feature analyses on yield (Yield_oil, %), nitrogen content (N_oil, %), and energy recovery rate (ER_oil, %) of bio-oil. The single-task results revealed that temperature (T, °C) was the most significant factor. Yield_oil and ER_oil reached their maximum values around 350 °C, while that of N_oil was around 280 °C. The multi-task results indicated that the GBR-ML model of the dataset#4 (n_estimators = 40, and max_depth = 7,) owed the highest average test R2 (0.84), which was suitable for developing a prediction application. Subsequently, through experimental validation with actual biomass, the best GBR multi-task ML model (T ≥ 300 °C, Yield_oil error < 11.75 %, N_oil error < 2.40 %, and ER_oil error < 9.97 %) based on the dataset#6 was obtained for HTL/co-HTL. With these steps, we developed an application for predicting the multi-object of bio-oil, which is scarcely reported in co-hydrothermal liquefaction studies.

Hypoxia and Singlet Oxygen Dual-Responsive Micelles for Photodynamic and Chemotherapy Therapy Featured with Enhanced Cellular Uptake and Triggered Cargo Delivery.

Introduction: Combination therapy provides better outcomes than a single therapy and becomes an efficient strategy for cancer treatment. In this study, we designed a hypoxia- and singlet oxygen-responsive polymeric micelles which contain azo and nitroimidazole groups for enhanced cellular uptake, repaid cargo release, and codelivery of photosensitizer Ce6 and hypoxia-activated prodrug tirapazamine TPZ (DHM-Ce6@TPZ), which could be used for combining Ce6-mediated photodynamic therapy (PDT) and PDT-activated chemotherapy to enhance the therapy effect of cancer. Methods: The hypoxia- and singlet oxygen-responsive polymeric micelles DHM-Ce6@TPZ were prepared by film hydration method. The morphology, physicochemical properties, stimuli responsiveness, in vitro singlet oxygen production, cellular uptake, and cell viability were evaluated. In addition, the in vivo therapeutic effects of the micelles were verified using a tumor xenograft mice model. Results: The resulting dual-responsive micelles not only increased the concentration of intracellular photosensitizer and TPZ, but also facilitated photosensitizer and TPZ release for enhanced integration of photodynamic and chemotherapy therapy. As a photosensitizer, Ce6 induced PDT by generating toxic singlet reactive oxygen species (ROS), resulting in a hypoxic tumor environment to activate the prodrug TPZ to achieve efficient chemotherapy, thereby evoking a synergistic photodynamic and chemotherapy therapeutic effect. The cascade synergistic therapeutic effect of DHM-Ce6@TPZ was effectively evaluated both in vitro and in vivo to inhibit tumor growth in a breast cancer mice model. Conclusion: The designed multifunctional micellar nano platform could be a convenient and powerful vehicle for the efficient co-delivery of photosensitizers and chemical drugs for enhanced synergistic photodynamic and chemotherapy therapeutic effect of cancer.

Application of an improved watershed algorithm based on distance map reconstruction in bean image segmentation.

As an important step in image processing, image segmentation can be used to determine the accuracy of object counts, and area and contour data. In addition, image segmentation is indispensable in seed testing research. Due to the uneven grey level of the original image, traditional watershed algorithms generate many incorrect edges, resulting in oversegmentation and undersegmentation, which affects the accuracy of obtaining seed phenotype information. The DMR-watershed algorithm, an improved watershed algorithm based on distance map reconstruction, is proposed in this paper. According to the grey distribution characteristics of the image, the grey reduction amplitude h was selected to generate the mask image with the same grey distribution trend as that of the original image. The original greyscale map was reconstructed with corresponding thresholds selected according to the false minima of different regions that are to be segmented, which generates an accurate distance map that eliminates the wrong edges. An adzuki bean (Vigna angularis L.) image was selected as the experimental material and the residual rate of the segmentation counting results of each algorithm was investigated in two cases of two-particle adhesion and multiparticle adhesion. The results of the proposed algorithm were compared with those of the traditional watershed algorithm, edge detection algorithm and concave point analysis algorithm which are commonly used for seed segmentation. In the case of two-particle adhesion, the residual rates of the watershed algorithm and edge detection algorithm were 0.233 and 0.275, respectively, while the residual rate of the concave point analysis algorithm was 0 which proved to be suitable for two-particle adhesion. In the case of multiparticle adhesion, the concave point analysis algorithm was not applicable because it would destroy the seed image. The residual rates of the watershed algorithm and edge detection algorithm were 0.063 and 0.188, respectively, while the residual rate of the proposed algorithm in the two-particle adhesion cases was 0 and the counting accuracy reached 100%, which proved the effectiveness of the proposed algorithm. The algorithm in this paper significantly improves the accuracy of image segmentation of adherent seeds, and provides a new reference for image segmentation processing in seed testing.

Critical closing pressure as a new hemodynamic marker of cerebral small vessel diseases burden.

Purpose: To investigate cerebrovascular hemodynamics, including critical closing pressure (CrCP) and pulsatility index (PI), and their independent relationship with cerebral small vessel disease (CSVD) burden in patients with small-vessel occlusion (SVO). Methods: We recruited consecutive patients with SVO of acute cerebral infarction who underwent brain magnetic resonance imaging (MRI), transcranial Doppler (TCD) and CrCP during admission. Cerebrovascular hemodynamics were assessed using TCD. We used the CSVD score to rate the total MRI burden of CSVD. Multiple regression analysis was used to determine parameters related to CSVD burden or CrCP. Results: Ninety-seven of 120 patients (mean age, 64.51 ± 9.99 years; 76% male) completed the full evaluations in this study. We observed that CrCP was an independent determinant of CSVD burden in four models [odds ratio, 1.41; 95% confidence interval (CI), 1.17-1.71; P < 0.001] and correlated with CSVD burden [ß (95% CI): 0.05 (0.04-0.06); P < 0.001]. In ROC analysis, CrCP was considered as a predictor of CSVD burden, and AUC was 86.2% (95% CI, 78.6-93.9%; P < 0.001). Multiple linear regression analysis showed that CrCP was significantly correlated with age [ß (95% CI): 0.27 (0.06 to 0.47); P = 0.012], BMI [ß (95% CI): 0.61 (0.00-1.22)] and systolic BP [ß (95% CI): 0.16 (0.09-0.23); P < 0.001]. Conclusions: CrCP representing cerebrovascular tension is an independent determinant and predictor of CSVD burden. It was significantly correlated with age, BMI and systolic blood pressure. These results provide new insights in the mechanism of CSVD development.

Deforming lanthanum trihydride for superionic conduction.

With strong reducibility and high redox potential, the hydride ion (H-) is a reactive hydrogen species and an energy carrier. Materials that conduct pure H- at ambient conditions will be enablers of advanced clean energy storage and electrochemical conversion technologies1,2. However, rare earth trihydrides, known for fast H migration, also exhibit detrimental electronic conductivity3-5. Here we show that by creating nanosized grains and defects in the lattice, the electronic conductivity of LaHx can be suppressed by more than five orders of magnitude. This transforms LaHx to a superionic conductor at -40 °C with a record high H- conductivity of 1.0 × 10-2 S cm-1 and a low diffusion barrier of 0.12 eV. A room-temperature all-solid-state hydride cell is demonstrated.

Application of logistic regression and machine learning methods for idiopathic inflammatory myopathies malignancy prediction.

OBJECTIVES: Malignancy is related to idiopathic inflammatory myopathies (IIM) and leads to a poor prognosis. Early prediction of malignancy is thought to improve the prognosis. However, predictive models have rarely been reported in IIM. Herein, we aimed to establish and use a machine learning (ML) algorithm to predict the possible risk factors for malignancy in IIM patients. METHODS: We retrospectively reviewed the medical records of 168 patients diagnosed with IIM in Shantou Central hospital, from 2013 to 2021. We randomly divided patients into two groups, the training sets (70%) for construction of the prediction model, and the validation sets (30%) for evaluation of model performance. We constructed six types of ML algorithms models and the AUC of ROC curves were used to describe the efficacy of the model. Finally, we set up a web version using the best prediction model to make it more generally available. RESULTS: According to the multi-variable regression analysis, three predictors were found to be the risk factors to establish the prediction model, including age, ALT<80U/L, and anti-TIF1-γ, and ILD was found to be a protective factor. Compared with five other ML algorithms models, the traditional algorithm logistic regression (LR) model was as good or better than the other models to predict malignancy in IIM. The AUC of the ROC using LR was 0.900 in the training set and 0.784 in the validation set. We selected the LR model as the final prediction model. Accordingly, a nomogram was constructed using the above four factors. A web version was built and can be visited on the website or acquired by scanning the QR code. CONCLUSIONS: The LR algorithm appears to be a good predictor of malignancy and may help clinicians screen, evaluate and follow up high-risk patients with IIM.

Catalyst-free synthesis of lithium hydride at room temperature.

An organic solvent-assisted catalyst-free mechanochemical reaction is developed to synthesize lithium hydride at mild gas pressures and room temperature. Studies show that the formation of intermediates on the surface of bulk lithium metal is crucial for the synthesis of high purity (>98%) LiH. This provides a new strategy for the large-scale production of lithium-based hydrogen storage materials.

Efficient Visible-Light-Driven Tetracycline Degradation and Cr(VI) Reduction over a LaNi1-xFexO3 (0 ≤ x ≤ 1)/g-C3N4 Type-II Heterojunction Photocatalyst.

The development of efficient, stable, and visible-light-responsive photocatalysts is crucial to address the pollution of water bodies by toxic heavy metal ions and organic antibiotics. Herein, a series of LaNi1-xFexO3/g-C3N4 heterojunction photocatalysts are prepared by a simple wet chemical method. Moreover, LaNi0.8Fe0.2O3/g-C3N4 composites are characterized by various methods, including structure, morphology, optical, and electrochemical methods and tetracycline degradation and photocatalytic reduction of Cr(VI) under visible light irradiation. Then, the photocatalytic performance of as-prepared LaNi0.8Fe0.2O3/g-C3N4 composites is evaluated. Compared with pure LaNi0.8Fe0.2O3 and g-C3N4, the LaNi0.8Fe0.2O3/g-C3N4 composite photocatalysts exhibit excellent photocatalytic performance due to synergy of doping and constructing heterojunctions. The results show that the doping of Fe ions can increase the concentration of oxygen vacancies, which is ultimately beneficial to the formation of electron traps. Moreover, the type-II heterojunction formed between LaNi0.8Fe0.2O3 and g-C3N4 effectively strengthens the separation and transfer of photoinduced carriers, thereby promoting photocatalytic activity. Furthermore, the photocatalytic activity of the LaNi0.8Fe0.2O3/g-C3N4 photocatalyst remains almost unchanged after three cycles, indicating long-term stability. Ultimately, the photocatalytic mechanism of the LaNi0.8Fe0.2O3/g-C3N4 composites is proposed.

One-Step Synthesis of Light Metal Nanoparticle from Metastable Complex.

Metal nanoparticles have attracted considerable scientific and technological interest in recent years, most related explorations and reports are focused on transition and noble metals. However, the synthesis and application of light metal nanoparticles represented by Mg have not been fully exploited, limited by their ultrahigh reactivity in air and preparation in harsh conditions. In this work, a simple and effective one-step organic solvent-assisted ball-milling process is developed to synthesize Mg and Li nanoparticles, which permits the formation of MgH2 in a hydrogen atmosphere in a one-step reaction process at ambient temperature. Further studies suggest that acetone chemisorbs on defects/surfaces of Mg during ball milling leading to the formation of a metastable magnesium complex, which significantly alters the physical and chemical characteristics of Mg grains. The formation of metastable complexes provides an attractive strategy to produce light metal nanoparticles and inspires the authors to study the interaction of organic solvents with light metals.

Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar.

Biochar produced from pyrolysis of biomass is a platform porous carbon material that have been widely used in many areas. Specific surface area (SSA) and total pore volume (TPV) are decisive to biochar application in hydrogen uptake, CO2 adsorption, and organic pollutant removal, etc. Engineering biochar by traditional experimental methods is time-consuming and laborious. Machine learning (ML) was used to effectively aid the prediction and engineering of biochar properties. The prediction of biochar yield, SSA, and TPV was achieved via random forest (RF) and gradient boosting regression (GBR) with test R2 of 0.89-0.94. ML model interpretation indicates pyrolysis temperature, biomass ash, and volatile matter were the most important features to the three targets. Pyrolysis parameters and biomass mixing ratios for biochar production were optimized via three-target GBR model, and the optimum schemes to obtain high SSA and TPV were experimentally verified, indicating the great potential of ML for biochar engineering.

Machine learning for hydrothermal treatment of biomass: A review.

Hydrothermal treatment (HTT) (i.e., hydrothermal carbonization, liquefaction, and gasification) is a promising technology for biomass valorization. However, diverse variables, including biomass compositions and hydrothermal processes parameters, have impeded in-depth mechanistic understanding on the reaction and engineering in HTT. Recently, machine learning (ML) has been widely employed to predict and optimize the production of biofuels, chemicals, and materials from HTT by feeding experimental data. This review comprehensively analyzed the application of ML for HTT of biomass and systematically illustrated basic ML procedure and descriptors for inputs and outputs of ML models (e.g., biomass compositions, operation conditions, yield and physicochemical properties of derived products) that could be applied in HTT. Moreover, this review summarized ML-aided HTT prediction of yield, compositions, and physicochemical properties of HTT hydrochar or biochar, bio-oil, syngas, and aqueous phase. Ultimately, future prospects were proposed to enhance predictive performance, mechanistic interpretation, process optimization, data sharing, and model application during ML-aided HTT.

Dual (pH- and ROS-) Responsive Antibacterial MXene-Based Nanocarrier for Drug Delivery.

In this study, a novel MXene (Ti3C2Tx)-based nanocarrier was developed for drug delivery. MXene nanosheets were functionalized with 3, 3'-diselanediyldipropionic acid (DSeDPA), followed by grafting doxorubicin (DOX) as a model drug to the surface of functionalized MXene nanosheets (MXene-Se-DOX). The nanosheets were characterized using scanning electron microscopy, atomic force microscopy (AFM), transmission electron microscopy, energy-dispersive X-ray spectroscopy (EDX), nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, X-ray diffraction, and zeta potential techniques. The drug-loading capacity (17.95%) and encapsulation efficiency (41.66%) were determined using ultraviolet-visible spectroscopy. The lateral size and thickness of the MXene nanosheets measured using AFM were 200 nm and 1.5 nm, respectively. The drug release behavior of the MXene-Se-DOX nanosheets was evaluated under different medium conditions, and the nanosheets demonstrated outstanding dual (reactive oxygen species (ROS)- and pH-) responsive properties. Furthermore, the MXene-Se-DOX nanosheets exhibited excellent antibacterial activity against both Gram-negative E. coli and Gram-positive B. subtilis.

ROS- and pH-Responsive Polydopamine Functionalized Ti3C2Tx MXene-Based Nanoparticles as Drug Delivery Nanocarriers with High Antibacterial Activity.

Premature drug release and poor controllability is a challenge in the practical application of tumor therapy, which may lead to poor chemotherapy efficacy and severe adverse effects. In this study, a reactive oxygen species (ROS)-cleavable nanoparticle system (MXene-TK-DOX@PDA) was designed for effective chemotherapy drug delivery and antibacterial applications. Doxorubicin (DOX) was conjugated to the surface of (3-aminopropyl)triethoxysilane (APTES)-functionalized MXene via an ROS-cleavable diacetoxyl thioketal (TK) linkage. Subsequently, the surfaces of the MXene nanosheets were coated with pH-responsive polydopamine (PDA) as a gatekeeper. PDA endowed the MXene-TK-DOX@PDA nanoparticles with superior biocompatibility and stability. The MXene-TK-DOX@PDA nanoparticles had an ultrathin planar structure and a small lateral size of approximately 180 nm. The as-synthesized nanoparticles demonstrated outstanding photothermal conversion efficiency, superior photothermal stability, and a remarkable extinction coefficient (23.3 L g-1 cm-1 at 808 nm). DOX exhibited both efficient ROS-responsive and pH-responsive release performance from MXene-TK-DOX@PDA nanoparticles due to the cleavage of the thioketal linker. In addition, MXene-TK-DOX@PDA nanoparticles displayed high antibacterial activity against both Gram-negative Escherichia coli (E. coli) and Gram-positive Bacillus subtilis (B. subtilis) within 5 h. Taken together, we hope that MXene-TK-DOX@PDA nanoparticles will enrich the drug delivery system and significantly expand their applications in the biomedical field.

Plant-scale biogas production prediction based on multiple hybrid machine learning technique.

The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting in low prediction accuracy when using traditional machine learning algorithms. In this study, a hybrid extreme learning machine (ELM) model was proposed to improve prediction accuracy by solving imbalanced data. The results showed that the best ELM model had a good prediction for validation data (R2 = 0.972), and the model was developed into the software (prediction error of 2.15 %). Furthermore, two parameters within a certain range (feed volume (FV) = 23-45 m3 and total volatile fatty acids of anaerobic digestion (TVFAAD) = 1750-3000 mg/L) were identified as the most important characteristics that positively affected biogas production. This study combines machine learning with data-balancing techniques and optimization algorithms to achieve accurate predictions of plant biogas production at various loads.

Psychological healing function of poetry appreciation based on educational psychology and aesthetic analysis.

With the development of society, the rapidly developing social environment has played a significant role in the particular group of college students. College students will inevitably suffer setbacks and psychological obstacles in their studies and daily life. This work aims to ameliorate college students' various mental illnesses caused by anxiety and confusion during the critical period of status transformation. Educational psychology theory, aesthetic theory, and poetry appreciation are applied to the mental health education of college students to obtain a satisfying psychological healing effect. First, this work summarizes the connotation and characteristics of college student's mental health and defines educational psychology. Secondly, the long tradition of Chinese poetry teaching is introduced. Besides, the theoretical basis of poetry therapy and aesthetic psychology is expounded, and foreign poetry is discussed. In addition, poetry appreciation is used to promote personality shaping and psychological healing of college students based on the theory of educational psychology and poetry appreciation psychotherapy. In addition, mental health education for college students is studied from the perspectives of psychological health, mental health education, college students' mental health education, and appreciation of ancient poetry. In addition, the principle and significance of college students' mental health education are discussed from the perspective of poetry appreciation. Finally, an experimental study is conducted on college students and patients in a specific hospital department by issuing questionnaires to verify the practical application effect of this method in psychotherapy. The survey results indicate that the scores of college students who have completed a one-semester poetry appreciation course in different dimensions of mental disorders are lower than those of those who have not completed the course. At the same time, in the scores of 16 personality traits, the positive trait scores of the experimental group are higher than those of the control group. Comparing scores before and after class also reflects the positive effect of poetry appreciation intervention on college students' personality shaping. It can be concluded that poetry appreciation has a strong effect on promoting college students' mental health and personality shaping and improving college students' psychological problems.

Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass.

Hydrothermal liquefaction (HTL) of high-moisture biomass or biowaste to produce bio-oil is a promising technology. However, nitrogen-heterocycles (NH) presence in bio-oil is a bottleneck to the upgrading and utilization of bio-oil. The present study applied the machine learning (ML) method (random forest) to predict and help control the bio-oil NH, bio-oil yield, and N content of bio-oil (N_oil). The results indicated that the predictive performance of the yield and N_oil were better than previous studies, achieving test R2 of 0.92 and 0.95, respectively. Acceptable predictive performance (test R2 of 0.82 and RMSE of 7.60) for the prediction of NH was also achieved. The feature importance analysis, partial dependence, and Shapely value were used to interpret the prediction models and study the NH formation mechanisms and behavior. Then, forward optimization of NH was implemented based on optimal predictive models, indicating the high potential of ML-aided bio-oil production and engineering.