ABSTRACT
Introduction: Depression is an affective disorder that contributes to a significant global burden of disease. Measurement-Based Care (MBC) is advocated during the full course management, with symptom assessment being an important component. Rating scales are widely used as convenient and powerful assessment tool, but they are influenced by the subjectivity and consistency of the raters. The assessment of depressive symptoms is usually conducted with a clear purpose and restricted content, such as clinical interviews based on the Hamilton Depression Rating Scale (HAMD), so that the results are easy to obtain and quantify. Artificial Intelligence (AI) techniques are used due to their objective, stable and consistent performance, and are suitable for assessing depressive symptoms. Therefore, this study applied Deep Learning (DL)-based Natural Language Processing (NLP) techniques to assess depressive symptoms during clinical interviews; thus, we proposed an algorithm model, explored the feasibility of the techniques, and evaluated their performance. Methods: The study included 329 patients with Major Depressive Episode. Clinical interviews based on the HAMD-17 were conducted by trained psychiatrists, whose speech was simultaneously recorded. A total of 387 audio recordings were included in the final analysis. A deeply time-series semantics model for the assessment of depressive symptoms based on multi-granularity and multi-task joint training (MGMT) is proposed. Results: The performance of MGMT is acceptable for assessing depressive symptoms with an F1 score (a metric of model performance, the harmonic mean of precision and recall) of 0.719 in classifying the four-level severity of depression and an F1 score of 0.890 in identifying the presence of depressive symptoms. Disscussion: This study demonstrates the feasibility of the DL and the NLP techniques applied to the clinical interview and the assessment of depressive symptoms. However, there are limitations to this study, including the lack of adequate samples, and the fact that using speech content alone to assess depressive symptoms loses the information gained through observation. A multi-dimensional model combing semantics with speech voice, facial expression, and other valuable information, as well as taking into account personalized information, is a possible direction in the future.
ABSTRACT
Selective semi-hydrogenation of acetylene is an extremely important reaction from both industrial and theoretical perspectives. Palladium, due to its unique chemical and physical properties, is the most active and currently irreplaceable metal for this reaction in industry, but the poor catalytic selectivity towards ethylene is also its inherent shortcoming. Introducing a secondary metal to tune a geometric and electronic structures of Pd nanoparticles and to create a synergistic effect is the most widely used strategy to effectively improve the overall catalytic performance of Pd-based catalysts. Thus, various supported Pd-based bimetallic catalysts for selective semi-hydrogenation of acetylene have been exploited in the past decade. Timely comparison, analysis, and summarizing of various preparation methods may offer a beneficial reference for the subsequent development of such catalysts. In this context, herein, the advances in synthesis strategies of catalysts, including nano-catalysts, single atom alloys (SAAs), as well as bimetallic dual atom catalysts are summarized systematically. Their advantages and disadvantages are comparatively discussed. Finally, future perspectives for the synthetic strategies of supported Pd-based bimetallic catalysts for selective semi-hydrogenation of acetylene are proposed.
ABSTRACT
Catalytic conversion of cellulose to liquid fuel and highly valuable platform chemicals remains a critical and challenging process. Here, bismuth-decorated ß zeolite catalysts (Bi/ß) were exploited for highly efficient hydrolysis and selective oxidation of cellulose to biomass-derived glycolic acid in an O2 atmosphere, which exhibited an exceptionally catalytic activity and high selectivity as well as excellent reusability. It was interestingly found that as high as 75.6% yield of glycolic acid over 2.3 wt% Bi/ß was achieved from cellulose at 180 °C for 16 h, which was superior to previously reported catalysts. Experimental results combined with characterization revealed that the synergetic effect between oxidation active sites from Bi species and surface acidity on H-ß together with appropriate total surface acidity significantly facilitated the chemoselectivity towards the production of glycolic acid in the direct, one-pot conversion of cellulose. This study will shed light on rationally designing Bi-based heterogeneous catalysts for sustainably generating glycolic acid from renewable biomass resources in the future.
Subject(s)
Cellulose , Zeolites , Cellulose/chemistry , Zeolites/chemistry , Bismuth , CatalysisABSTRACT
Novel zinc-palladium-porphyrin bimetal metal-organic framework (MOF) nanosheets were directly synthesized by coordination chelation between Zn(II) and Pd(II) tetra(4-carboxyphenyl)porphin (TCPP(Pd)) using a solvothermal method. Furthermore, a serial of carbon nanosheets supported Pd-Zn intermetallics (Pd-Zn-ins/CNS) with different Pd: Zn atomic ratios were obtained by one-step carbonization under different temperature using the prepared Zn-TCPP(Pd) MOF nanosheets as precursor. In the carbonization process, Pd-Zn-ins went through the transformation from PdZn (650 °C) to Pd3.9Zn6.1 (~950 °C) then to Pd3.9Zn6.1/Pd (1000 °C) with the temperature increasing. The synthesized Pd-Zn-ins/CNS were further employed as catalysts for selective hydrogenation of acetylene. Pd3.9Zn6.1 showed the best catalytic performance compared with other Pd-Zn intermetallic forms.
