化学系统性思维的表征手段与改革实践*

化学系统性思维强调化学子系统之间,以及化学系统与其他学科系统之间的关系,有助于学习者整合、应用化学知识解释化学现象、解决化学问题。内外交织的多个不同系统很容易让学生迷失在纷繁复杂的概念体系中,需要借助SOCME,OPM,BOTG,CLD,SFD等可视化图形工具厘清各个系统之间的关系,以表征化学系统性思维。在明确化学系统性思维内涵的基础上,开展“化学平衡”教学改革,探索绿色化学课程建设,开展游戏化学习、服务性学习、深度学习、项目学习、工作坊或研讨会,有助于化学系统性思维培养实践的改革与落地。横向关联化学系统与其他学科系统的关系,纵向深入分析化学子系统之间的关系,是进一步开展化学系统性思维教学的关键。这就需要多学科的协同攻关,既要关注化学知识的社会应用,也要抓住化学学科本质和特征,才可以围绕化学概念和社会问题,建构纵横交织的多系统影响关系,促进学生化学系统性思维的发展。     

深度学习视域下“线上线下”混合式教学模式应用实践*——以“无机与分析化学”课程为例

深度学习视域下“线上线下”混合式教学模式的核心是学生的深度参与、高级学习策略的使用及教师的优质讲授,最终实现学生深度学习能力的养成。“无机与分析化学”线上线下混合式教学通过在线预习、课堂讨论、课前课后测试、实践教学等多种教学设计,让学生化“被动”学习为“主动”学习,深度参与,锻炼学生自主学习能力,增强学生团队协作及解决复杂工程问题的能力,启发学生创新应用能力。     

基于“虚拟化学实验室”的在线课堂深度学习*—以“价层电子对互斥模型(VSEPR)”在线教学设计为例

在线课堂作为一种远程教育方式,往往会导致学生缺乏学习社区感。面对抽象的理论性知识,学生易陷入一种“离身”的困境。基于此,借助虚拟化学实验室,设计一堂“身临其境”“做中学”的价层电子对互斥模型课,实现“抽象内容具身认知化”“在线学习互动现实化”的在线课堂深度学习。     

ProteinUnet—An efficient alternative to SPIDER3‐single for sequence‐based prediction of protein secondary structures

Predicting protein function and structure from sequence remains an unsolved problem in bioinformatics. The best performing methods rely heavily on evolutionary information from multiple sequence alignments, which means their accuracy deteriorates for sequences with a few homologs, and given the increasing sequence database sizes requires long computation times. Here, a single‐sequence‐based prediction method is presented, called ProteinUnet, leveraging an U‐Net convolutional network architecture. It is compared to SPIDER3‐Single model, based on long short‐term memory‐bidirectional recurrent neural networks architecture. Both methods achieve similar results for prediction of secondary structures (both three‐ and eight‐state), half‐sphere exposure, and contact number, but ProteinUnet has two times fewer parameters, 17 times shorter inference time, and can be trained 11 times faster. Moreover, ProteinUnet tends to be better for short sequences and residues with a low number of local contacts. Additionally, the method of loss weighting is presented as an effective way of increasing accuracy for rare secondary structures.     

Novel Processes for the Extraction of Phenolic Compounds from Olive Pomace and Their Protection by Encapsulation

Olive pomace, the solid by-product derived from olive oil production consists of a high concentration of bioactive compounds with antioxidant activity, such as phenolic compounds, and their recovery by applying innovative techniques is a great opportunity and challenge for the olive oil industry. This study aimed to point out a new approach for the integrated valorization of olive pomace by extracting the phenolic compounds and protecting them by encapsulation or incorporation in nanoemulsions. Innovative assisted extraction methods were evaluated such as microwave (MAE), homogenization (HAE), ultrasound (UAE), and high hydrostatic pressure (HHPAE) using various solvent systems including ethanol, methanol, and natural deep eutectic solvents (NADESs). The best extraction efficiency of phenolic compounds was achieved by using NADES as extraction solvent and in particular the mixture choline chloride-caffeic acid (CCA) and choline chloride-lactic acid (CLA); by HAE at 60 °C/12,000 rpm and UAE at 60 °C, the total phenolic content (TPC) of extracts was 34.08 mg gallic acid (GA)/g dw and 20.14 mg GA/g dw for CCA, and by MAE at 60 °C and HHPAE at 600 MPa/10 min, the TPC was 29.57 mg GA/g dw and 25.96 mg GA/g dw for CLA. HAE proved to be the best method for the extraction of phenolic compounds from olive pomace. Microencapsulation and nanoemulsion formulations were also reviewed for the protection of the phenolic compounds extracted from olive pomace. Both encapsulation techniques exhibited satisfactory results in terms of encapsulation stability. Thus, they can be proposed as an excellent technique to incorporate phenolic compounds into food products in order to enhance both their antioxidative stability and nutritional value.     

Furfural and 5-Hydroxymethylfurfural Production from Sugar Mixture Using Deep Eutectic Solvent/MIBK System

Choline chloride (ChCl) / glycolic acid (GA) deep eutectic solvent (DES) media with high water content but without any additional catalyst are introduced in furfural and 5-hydroxymethylfurfural (HMF) production. The effects of water content, reaction time, and reaction temperature are investigated with two feedstocks: a glucose/xylose mixture and birch sawdust. Based on the results, 10 equivalent quantities of water (32.9 wt.%) were revealed to be beneficial for conversions without rupturing the DES structure. The optimal reaction conditions were 160 °C and 10 minutes for the sugar mixture and 170 °C and 10 minutes for birch sawdust in a microwave reactor. High furfural yields were achieved, namely 62 % from the sugar mixture and 37.5 % from birch sawdust. HMF yields were low, but since the characterization of the solid residue of sawdust, after DES treatment, was revealed to contain only cellulose (49 %) and lignin (52 %), the treatment could be potentially utilized in a biorefinery concept where the main products are obtained from the cellulose fraction. Extraction of products into the organic phase (methyl isobutyl ketone, MIBK) during the reaction enabled the recycling of the DES phase, and yields remained high for three runs of recycling.     

Prediction of Chromatography Conditions for Purification in Organic Synthesis Using Deep Learning

In this research, a process for developing normal-phase liquid chromatography solvent systems has been proposed. In contrast to the development of conditions via thin-layer chromatography (TLC), this process is based on the architecture of two hierarchically connected neural network-based components. Using a large database of reaction procedures allows those two components to perform an essential role in the machine-learning-based prediction of chromatographic purification conditions, i.e., solvents and the ratio between solvents. In our paper, we build two datasets and test various molecular vectorization approaches, such as extended-connectivity fingerprints, learned embedding, and auto-encoders along with different types of deep neural networks to demonstrate a novel method for modeling chromatographic solvent systems employing two neural networks in sequence. Afterward, we present our findings and provide insights on the most effective methods for solving prediction tasks. Our approach results in a system of two neural networks with long short-term memory (LSTM)-based auto-encoders, where the first predicts solvent labels (by reaching the classification accuracy of 0.950 ± 0.001) and in the case of two solvents, the second one predicts the ratio between two solvents (R² metric equal to 0.982 ± 0.001). Our approach can be used as a guidance instrument in laboratories to accelerate scouting for suitable chromatography conditions.     

Metal-organic framework functionalized with deep eutectic solvent for solid-phase extraction of Rhodamine 6G in water and cosmetic products

An NH₂-MIL-53(Al)-DES(ChCl-Urea) nanocomposite was synthesized for extraction and determination of Rhodamine (Rh) 6G from environmental and cosmetic samples. The deep eutectic solvent (DES) was prepared by mixing choline chloride and urea in a mole ratio of 1:2. NH₂-MIL-53(Al)-DES(ChCl-Urea) nanocomposite was synthesized using the impregnation method at a ratio of 60:40 (w/w). The optimum conditions were determined after NH₂-MIL-53(Al)-DES(ChCl-Urea) characterization was performed. The optimum conditions were determined as pH 8, adsorbent amount of 15 mg, total adsorption-desorption time of 6 min, and enrichment factor of 20. The recovery values of the solid-phase extraction method for water and cosmetic samples under optimum conditions were between 95% and 106%. NH₂-MIL-53(Al)-DES(ChCl-Urea) nanocomposite was an economically advantageous adsorbent because of its reusability of 15 times. All analyses were performed using the ultraviolet-visible spectrophotometer. The linear range, limit of detection, and limit of quantification of the method were 100–1000, 9.80, and 32.68 μg/L, respectively. The obtained results showed that the synthesized nanocomposite is a suitable adsorbent for the determination of Rh 6G in water and cosmetic samples. The real sample applications were verified with the high-performance liquid chromatography system.     

Miniaturized ternary deep eutectic solvent-based matrix solid-phase dispersion: A green sample preparation method for the determination of chlorophenols in river sediment

New ternary deep eutectic solvents were prepared and applied as efficient green dispersing solvents in miniaturized matrix solid-phase dispersion to extract chlorophenols from river sediments for the first time. High-performance liquid chromatography coupled with a photodiode array detector was used to analyze the target analytes. The significant factors affecting the extraction were optimized as follows: dispersant (100 mg), sample (100 mg), ternary eutectic solvents (150 μl), grinding for 1 min, 450 μl of acetonitrile as the elution solvent, and vortex mixing for 20 s. Under the optimal conditions, the method exhibited excellent linearity (correlation coefficient > 0.9980), low limits of detection between 1.039–2.478 μg/g, and extraction recoveries between 93.9% and 99.2%. Furthermore, the method demonstrated excellent precision in the intra- and inter-day analysis with a relative standard deviation below 6%. When compared to conventional extraction techniques, the miniaturized matrix solid-phase dispersion considerably reduced samples and solvent usag, offering important environmental benefits. The green profile of the method was assessed using the complementary green analytical procedure index tool confirming its eco-friendship. The technique was finally employed to evaluate sediment samples from three distinct locations along the Zuibaiji River, indicating its applicability for monitoring environmental samples.     

Choline chloride–ZnCl2: Recyclable and efficient deep eutectic solvent for the [2+3] cycloaddition reaction of organic nitriles with sodium azide

Herein we report first time choline chloride–zinc chloride based deep eutectic solvent is a green and efficient reaction medium for the [2+3] cycloaddition reaction of organic nitriles with sodium azide to afford the corresponding 5-substituted 1H-tetrazoles. The present method provides an environment-friendly route, short reaction times with excellent yields, safe process, and simple workup which make this method an attractive and useful contribution to the existing conventional processes for the synthesis of 5-substituted 1H-tetrazoles.