• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    深度學(xué)習(xí)引導(dǎo)的高通量分子篩選用于鍶銫的選擇性配位

    2023-11-01 06:58:36張智淵邱雨晴畢可鑫胡孔球戴一陽石偉群
    核化學(xué)與放射化學(xué) 2023年5期
    關(guān)鍵詞:高能物理四川大學(xué)工程學(xué)院

    張智淵,董 越,邱雨晴,畢可鑫,胡孔球,戴一陽,周 利,劉 沖,*,吉 旭,石偉群

    1.四川大學(xué) 化學(xué)工程學(xué)院,四川 成都 610065;2.中國(guó)科學(xué)院 高能物理研究所,北京 100049

    To meet the carbon neutral agenda globally, development and expansion of nuclear power remains an ideal option to provide electricity for an ever-growing world population while minimizing environmental impacts during operation[1]. State-of-the-art nuclear technologies that hold the promise of a future of clean energy require a closed fuel cycle for safety and sustainability reasons among others, necessitating more research on advanced reprocessing of spent nuclear fuel(SNF). Over the past seven decades, various SNF reprocessing processes have been established to recover critical radionuclides and to reduce radioactive wastes, such as PUREX(plutonium-uranium extraction), UNEX(universal extraction), FPEX(fission product extraction), etc[2]. Among all radionuclides in the high-level liquid waste(HLLW) generated from SNF reprocessing,90Sr and137Cs are major sources for the heat load and radiation[3-5]. Therefore, processes like UNEX were employed to separate90Sr and137Cs simultaneously to lessen the raffinates’ radioactivity, beneficial for downstream operations[3]. Additionally, a further separation between chemically similar90Sr and137Cs could produce valuable materials for radiation therapy, radioisotope thermoelectric generators, industrial gauging devices, etc[3].

    In fact, for many SNF reprocessing scenarios, differential coordinative chemical properties of various species are usually the basis to realize successful separation. The same principle is applicable for Sr/Cs separation[6-7], where a large number of coordinating ligands need to be assessed and compared to identify ones with selectivity to achieve preferential coordination(for extraction or crystallization). In our previous work, a machine-learning-guided methodology was developed to rank bridging linkers to form coordination polymers for crystallizing separation of Sr2+over Cs+, in which strengths of coordination bonds were found to be critical in evaluating and comparing different linker molecules’ coordinative affinities and selectivity[8]. Continuing on that, we now propose a more comprehensive study to reliably assess and rank ligands based on their coordinative affinities toward Sr/Cs, using a deep learning(DL) architecture. Specifically, we employ atransformerframework that originated in the field of natural language processing(NLP)[9], specializing in the identification of meaningful segments(e.g., functional groups in molecules) and extraction of contextual information(e.g., structure-property relationships). Moreover, considering the complexities of DL(i.e.,transformer) models and demanding computational burdens thereof, Bayesian optimization(BO) was applied to improve efficiency of the training process[10-11].

    An overview of the workflow is shown in Fig.1. The present study started with mining the crystal data of the Cambridge Structural Database(CSD)[12], from which we retrieved information regarding relevant metal-ligand(M-L, M=select 1A/2A metals) pairs and corresponding ligands for subsequent analysis. Next, we trained DL models(i.e.,transformer) using said M-L pairs, which enabled us to systematically evaluate the coordination capabilities of the ligands toward metals of interest. Specifically,to optimize the DL models efficiently, the hyperparameters(HPs) were tuned employing a BO approach. Finally, we ranked the ligands according to their predicted(differential) coordinative affinities for Sr and Cs. We expect that, for a certain ligand, the more different affinities it exhibits toward Cs over Sr(or vice versa), the higher degree of selective coordination is anticipated, hence better separation capability.

    Fig.1 Overview of deep-learning-centered protocol to identify and rank candidate ligands for selective Sr/Cs coordination

    1 Methods

    1.1 Data mining and molecular representation

    Using the CSD Python API[12]provided by the Cambridge Crystallographic Data Centre(CCDC), we were able to extract all M-L pairs and corresponding ligands from crystallographic data that contain 1A(Na, K, Rb, Cs) and 2A(Mg, Ca, Sr, Ba) elements. Then, these data were pre-processed to remove 74 CSD-predefined solvents[13], self-defined free anions/gas molecules[8], and standalone atoms without linkage to ligands. Subsequently, all M-L pairs and ligands were extracted from the pre-processed data. Data pre-processing and extraction of M-L pairs and ligands are illustrated in an example in Fig.2. Next, to be used as input for the DL models, the extracted M-L pairs were linearly represented by the canonical simplified molecular-input line-entry system(canonical SMILES)[14-15]. Along with the molecular structures, structural parameters like bond lengths, bond angles, and coordination numbers could also be extracted from the datasets, among which the coordination bond length was selected as the representative parameter to describe the coordinative affinity or strength of interaction between M and L in a given M-L pair. Additionally, we also extracted 9 169 ligands that would be used for virtual M-L pair generation.

    Fig.2 An example of data pre-processing and molecular structure extraction

    1.2 Transformer architecture

    We usedtransformer[9, 16]as the DL architecture to model the relationship between the molecular structures of M-L pairs and the coordinative affinity(i.e., bond length) of the ligands involved. As shown in Fig.3, thetransformerarchitecture is composed of a word embedding layer, a positional encoding layer, the main body(encoder/decoder) and a multilayer perceptron(MLP) predictor.

    Fig.3 Schematic illustration of transformer architecture

    Specifically, the word embedding layer[16]can convert discrete symbolic representation(i.e., the abovementioned SMILES symbols) to continuous vectors, as required by thetransformerarchitecture. The positional encoding layer[17]

    compiles the positional information for the sequence of characters in the SMILES, creating another set of vectors to be used as input fortransformermain module. The main oftransformeris composed of encoder and decoder[16]. In short, the encoder can recognize certain combinations of characters in a SMILES sequence, which usually have higher abstract meanings in chemistry than individual characters. Then the decoder would identify which combinations are important for the target(i.e., coordination bond length in our work). Finally, the coordination bond lengths would be predicted by the MLP predictor according to the decoder-proposed important structural combinations.

    It should be noted, as illustrated in Fig.3, that two inputs are required for the encoder and decoder oftransformer, respectively. In general, the input for encoder is the complete original SMILES in our dataset. For the decoder, the input should provide information about the specific target of prediction, that is the coordination bond length of a specific bond, considering there are possibly multiple coordination bonds in a given M-L pair.

    1.3 Model training and Bayesian optimization

    To train thetransformermodels, all M-L pairs were divided into train set, validation set and test set in a ratio of 8∶1∶1. The train set was used to train the models; the validation set was used to validate the performance of trained model and as the “target” for HP optimization; the test set was to test the generalizability of the optimal model. Thetransformermodels were trained by a back-propagation(BP) algorithm[18]and gradient descent to minimize the mean-square-error loss function:

    (1)

    Table 1 HP tuning space

    To improve efficiency, we proposed to apply BO algorithm[19-20]to optimize the HPs. As shown in Fig.4(a)(using only a one-dimensional objective function as an example), Gaussian process(GP)[21]and expected improvement(EI)[22]were chosen as the surrogate function and acquisition function, respectively. In our higher-dimensional objective function of HP tuning, GP fits the(unknown) objective function with estimated uncertainties. Consequently, EI proposes the most valuable samples(HPs) to try next. For a given dataset, optimization was performed by applying BO process for 8 batches(4 models per batch). Comparing to manual optimization(Fig.4(b)), the BO approach(Fig.4(c)) was shown to be superior to optimize thetransformermodels, achieving a higherr2(0.927 vs 0.856 for manual) after 8 batches of optimization. As illustrated in Fig.4(d) where the HPs were reduced to a two-dimensional space by t-distributed Stochastic Neighbor Embedding(t-SNE)[23], BO could cover wider parameter space, beneficial for avoiding local optima which constantly challenge manual optimization strategies.

    Fig.4 An example of Bayesian optimization on a one-dimensional objective function(top, black curve), using a GP surrogate function(top, magenta curve and pink area) to generate an EI acquisition function(bottom)(a); comparison of manual optimization(b) and Bayesian optimization(c) on coefficients of determination(r2, scattered points) for transformer models, blue line indicates the averaged r2 for models in the current batch, red line indicates the max r2 of trained models so far; distribution of HPs after dimension reduction by t-SNE, where blue and red dots are HPs selected by BO algorithm and manual optimization, respectively(d)

    1.4 Ligand assessment

    Based on 9 169 ligand molecules extracted from CSD(section 1.1) that contain 12 common coordinating groups(listed in Table 2), Cs-L/Sr-L pairs were generated by virtually bonding the coordinating atoms(e.g., N/O) with Sr/Cs and subsequently represented using SMILES. For the 2×9 169 virtual M-L pairs(i.e., 9 169 Sr-L pairs and 9 169 Cs-L pairs) generated, there is always a Cs-L pair for any Sr-L pair, sharing the identical molecular structure except for the metal, and vice versa, therefore enabling us to predict said L’s different affinity toward Sr/Cs. We grouped each of the 9 169 Sr-L/Cs-L pairs, denoted asGi, wherei=1, 2,…, 9 169.

    Table 2 Counts of coordinating functional groups for generating virtual (Sr, Cs)-L pairs

    (2)

    (3)

    2 Results and discussion

    2.1 Datasets

    After mining of structural data of targeted 1A/2A elements in CSD, we extracted 33 095 M-L pairs, in which 19 467 were mono-coordinated and 13 628 multi-coordinated. A statistical summary is given in Table 3, broken into different elements. In total, these M-L pairs contained 98 411 coordination bonds, i.e., 98 411 samples. It is widely accepted that, for deep learning, to contain as many relevant samples as possible in the training dataset is always beneficial for the model performance[25-27]. Therefore, we argue that all 33 095 M-L pairs(not just ones with Sr/Cs) should be used to train and optimizetransformermodels. Specifically, those M-L pairs containing Na, K, Rb, Mg, Ca and Ba were considered relevant because: 1) they provided information about molecular structures of ligands; and 2) periodicity-dictated elemental similarity in 1A(Na, K, Rb, Cs) and 2A(Mg, Ca, Sr, Ba) groups should lead to similar coordination properties(e.g., coordinating function groups and atoms).

    Table 3 Statistics of extracted M-L pairs of specified 1A/2A elements

    In order to experimentally confirm this empirical rule and justify our choice of expanded datasets instead of focusing on directly relevant(Sr, Cs)-L samples,transformermodels based on two datasets(i.e., all 33 095 M-L pairs and 4 271(Sr, Cs)-L pairs) were trained and optimized using the same protocol(section 1.3). The mean absolute error(MAE) of the best model based on the dataset of all 33 095 M-L pairs was 0.076 6 ?(1 ?=0.1 nm), 14% less than the best MAE based on 4 271(Sr, Cs)-L pairs, which was 0.088 9 ?.

    2.2 Transformer model

    As described in section 1.3 and illustrated in Fig.5(a), based on all 33 095 M-L pairs,transformermodel with the highest performance(r2= 0.927 and MAE=0.077 5 ?) on the test dataset was the 21stmodel(its HPs are characterized by the underlined numbers in Table 1) produced by the BO process. The regression diagram and the distribution histogram of absolute error, comparing the model-predicted values against actual coordination bond lengths(i.e., target) for samples in the test dataset, are shown in Fig.5(a) and 5(b), respectively. Overall, thetransformerarchitecture, expanded dataset and BO algorithm have generated a better prediction model comparing to our previous approach[8].

    Fig.5 Regression diagram between prediction and target(higher density of scatters is brighter-colored)(a); histogram of absolute error between prediction and target(b)

    2.3 Ligand and functional group analysis

    Fig.6 Top 10 identified M-L pairs with the largest

    Further, as functional groups usually play critical roles in determining the coordination properties of ligand molecules, the distribution of most frequent 8 coordinating groups(out of 12 that were listed in Table 2) on the ranking list was analyzed. The mean probability density(MPD) of each functional group appearing in the top 1%, 1%-10%, 10%-50% and bottom 50% of the ranking list was calculated by the following:

    (4)

    WhereNis the total number of virtual M-L pairs(i.e., 9 169),gis the index of functional groups(g=1, 2, …, 8),jis the index of ranking percentages(i.e., 1%, 1%-10%, 10%-50% and bottom 50%, respectively),ngis the amount of functional groupg,pjrepresents the percentage span ofj,cg,jis the count of functional groupgin percentage(range)j. As shown in Fig.7, for any functional groupgin ranking positionj, if MPD(g,j)>1(indicated by the broken line), the occurrence probability(OP) ofginjis greater that the OP ofgin the total list. Therefore, we could conclude that phosphoric acid group, with 3.82 MPD in top 1% and 2.10 MPD in 1%-10%, had the highest probability to be incorporated in a Sr-selective ligand[28-30]. Next in line, hydroxyl, ketone and ether groups showed moderate selectivity toward Sr. Surprisingly, the other common acidic functional groups(i.e., sulfonic acid and carboxylic acid), were not predicted to be coordinatively selective for Sr, which was counterintuitive according to the Hard and Soft Acids and Bases principle as Sr2+is considered a harder Lewis acid than Cs+.

    Fig.7 MPD of 8 frequent functional groups in specified ranking percentages

    3 Conclusions

    In summary, tackling the Sr/Cs separation challenge in SNF reprocessing, we have conducted a deep-learning-guided comprehensive study from the perspective of coordination chemistry. Based on crystal structural data of Sr/Cs and select congeners in respective groups, with the aid of Bayesian optimization, we developedtransformermodels with high performances in predicting coordination bond lengths which were identified as a figure of merit for assessing coordinative affinities. As a proof of concept, we analyzed 9 169 CSD-registered ligands and predicted their differential coordination capabilities toward Sr/Cs, as demonstrated in the top 10 molecular structures and a detailed analysis of functional groups with different potentials for selective coordination toward Sr over Cs. The ranking list of ligands and identification of promising functional groups(e.g., phosphoric acid) would be beneficial for downstream experimental screening and evaluation in separation scenarios.

    猜你喜歡
    高能物理四川大學(xué)工程學(xué)院
    福建工程學(xué)院
    盛宴已經(jīng)結(jié)束
    福建工程學(xué)院
    四川大學(xué)西航港實(shí)驗(yàn)小學(xué)
    福建工程學(xué)院
    福建工程學(xué)院
    百年精誠(chéng) 譽(yù)從信來——走進(jìn)四川大學(xué)華西眼視光之一
    四川大學(xué)華西醫(yī)院
    高能物理中的數(shù)據(jù)分析
    四川大學(xué)信息顯示研究所
    液晶與顯示(2014年2期)2014-02-28 21:12:58
    欧美高清性xxxxhd video| 国产免费又黄又爽又色| h日本视频在线播放| 亚洲精品乱码久久久久久按摩| 丰满人妻一区二区三区视频av| 一级二级三级毛片免费看| 卡戴珊不雅视频在线播放| 51国产日韩欧美| 97在线人人人人妻| www.av在线官网国产| 日产精品乱码卡一卡2卡三| 国产成人精品婷婷| 简卡轻食公司| 男女无遮挡免费网站观看| 插逼视频在线观看| 久久久成人免费电影| 亚洲色图综合在线观看| 99热全是精品| 中文资源天堂在线| 亚洲欧美日韩无卡精品| 日本av手机在线免费观看| 国产老妇伦熟女老妇高清| 亚洲人成网站在线播| 99re6热这里在线精品视频| 久久精品人妻少妇| 国产精品爽爽va在线观看网站| 亚洲国产精品成人久久小说| 久热这里只有精品99| 日本-黄色视频高清免费观看| 国产精品av视频在线免费观看| 日本爱情动作片www.在线观看| 人妻系列 视频| 成人一区二区视频在线观看| av在线蜜桃| 亚洲一级一片aⅴ在线观看| 大片免费播放器 马上看| 欧美日韩亚洲高清精品| 免费大片黄手机在线观看| 精品人妻偷拍中文字幕| 成人影院久久| 干丝袜人妻中文字幕| 亚洲精华国产精华液的使用体验| 中文欧美无线码| 18禁在线播放成人免费| 妹子高潮喷水视频| 九草在线视频观看| 久久久成人免费电影| 日韩强制内射视频| 国产无遮挡羞羞视频在线观看| 久久青草综合色| 肉色欧美久久久久久久蜜桃| 麻豆成人av视频| 国产乱人视频| 欧美性感艳星| 嘟嘟电影网在线观看| 国产伦理片在线播放av一区| 一区在线观看完整版| 超碰av人人做人人爽久久| 99热全是精品| 亚洲性久久影院| 只有这里有精品99| 久久精品久久精品一区二区三区| 伊人久久国产一区二区| 精品熟女少妇av免费看| 一级a做视频免费观看| 久久久久网色| 久久久欧美国产精品| 久久精品人妻少妇| 亚洲精品国产色婷婷电影| 秋霞在线观看毛片| 18+在线观看网站| 边亲边吃奶的免费视频| 舔av片在线| 日韩一区二区三区影片| 亚洲久久久国产精品| 亚洲欧美精品专区久久| 亚洲aⅴ乱码一区二区在线播放| 91精品国产九色| 在线精品无人区一区二区三 | 色视频在线一区二区三区| 99热这里只有是精品在线观看| 国产精品不卡视频一区二区| 婷婷色麻豆天堂久久| 日韩强制内射视频| a级毛色黄片| 尾随美女入室| 3wmmmm亚洲av在线观看| 一区二区三区乱码不卡18| 久久精品国产亚洲av天美| 伦理电影大哥的女人| 亚洲精品日韩av片在线观看| 一二三四中文在线观看免费高清| av在线老鸭窝| 亚洲精品视频女| 国产精品免费大片| 国产精品伦人一区二区| 全区人妻精品视频| 大片电影免费在线观看免费| 国产精品国产三级国产专区5o| 少妇的逼水好多| 国产av码专区亚洲av| 成人国产麻豆网| 国产爽快片一区二区三区| av在线app专区| 乱码一卡2卡4卡精品| 久久精品国产鲁丝片午夜精品| 日韩国内少妇激情av| 国产伦精品一区二区三区四那| 九草在线视频观看| 日本午夜av视频| 九九久久精品国产亚洲av麻豆| 久久99热这里只频精品6学生| 国产中年淑女户外野战色| 国产无遮挡羞羞视频在线观看| 性色avwww在线观看| 国产乱来视频区| 国产免费视频播放在线视频| 久久精品夜色国产| av在线app专区| 人妻制服诱惑在线中文字幕| 亚洲精品aⅴ在线观看| 视频区图区小说| 18禁在线播放成人免费| 水蜜桃什么品种好| 精品亚洲成a人片在线观看 | 精华霜和精华液先用哪个| 亚洲内射少妇av| 午夜老司机福利剧场| 欧美丝袜亚洲另类| av在线老鸭窝| 国产白丝娇喘喷水9色精品| 日日啪夜夜撸| 国产欧美另类精品又又久久亚洲欧美| 亚洲成人手机| 美女高潮的动态| 亚洲第一av免费看| 少妇高潮的动态图| 亚洲精品自拍成人| 97超视频在线观看视频| 亚洲,一卡二卡三卡| 日韩 亚洲 欧美在线| 中文字幕人妻熟人妻熟丝袜美| 欧美+日韩+精品| 国产爱豆传媒在线观看| 丰满人妻一区二区三区视频av| 一二三四中文在线观看免费高清| 免费在线观看成人毛片| 国产淫语在线视频| 免费av不卡在线播放| 91aial.com中文字幕在线观看| 啦啦啦在线观看免费高清www| 天天躁日日操中文字幕| 国产v大片淫在线免费观看| 日韩欧美一区视频在线观看 | 久久99精品国语久久久| 精品一区二区免费观看| 亚洲aⅴ乱码一区二区在线播放| 中文字幕制服av| 蜜桃在线观看..| 少妇的逼水好多| 三级国产精品欧美在线观看| 少妇丰满av| 18禁在线播放成人免费| 少妇 在线观看| 亚洲人成网站高清观看| 看十八女毛片水多多多| 街头女战士在线观看网站| 一级片'在线观看视频| videossex国产| 亚洲人成网站在线播| 国产成人freesex在线| 婷婷色麻豆天堂久久| 麻豆国产97在线/欧美| 大香蕉97超碰在线| 亚洲色图综合在线观看| 男男h啪啪无遮挡| 建设人人有责人人尽责人人享有的 | 美女内射精品一级片tv| 亚洲精品中文字幕在线视频 | 精品人妻一区二区三区麻豆| 五月开心婷婷网| 一区二区三区精品91| 国产精品三级大全| av不卡在线播放| 国产大屁股一区二区在线视频| 黄色一级大片看看| av国产免费在线观看| 激情 狠狠 欧美| 水蜜桃什么品种好| h视频一区二区三区| 日本免费在线观看一区| 制服丝袜香蕉在线| 亚洲av欧美aⅴ国产| 最新中文字幕久久久久| 丰满迷人的少妇在线观看| 亚洲四区av| 国产高清三级在线| h日本视频在线播放| 最黄视频免费看| 成人18禁高潮啪啪吃奶动态图 | 免费观看在线日韩| 在线观看一区二区三区| 少妇裸体淫交视频免费看高清| 亚洲av综合色区一区| 久久av网站| 国产亚洲5aaaaa淫片| 色婷婷av一区二区三区视频| 一级黄片播放器| 女人久久www免费人成看片| 777米奇影视久久| 麻豆精品久久久久久蜜桃| 久久女婷五月综合色啪小说| 亚洲av电影在线观看一区二区三区| 免费黄网站久久成人精品| 成人毛片a级毛片在线播放| 少妇裸体淫交视频免费看高清| 国产高清三级在线| 少妇人妻 视频| 丝袜脚勾引网站| 亚洲无线观看免费| 日本猛色少妇xxxxx猛交久久| 麻豆乱淫一区二区| 插逼视频在线观看| 久久韩国三级中文字幕| 午夜视频国产福利| 菩萨蛮人人尽说江南好唐韦庄| 亚洲精品成人av观看孕妇| 夫妻性生交免费视频一级片| 国产综合精华液| 免费黄频网站在线观看国产| 亚洲成人中文字幕在线播放| 最近2019中文字幕mv第一页| 久久久久久久久久人人人人人人| 日本爱情动作片www.在线观看| 大又大粗又爽又黄少妇毛片口| 国产午夜精品久久久久久一区二区三区| 一本久久精品| www.av在线官网国产| 夫妻午夜视频| 丝瓜视频免费看黄片| .国产精品久久| 亚洲国产日韩一区二区| 内射极品少妇av片p| 成人高潮视频无遮挡免费网站| 26uuu在线亚洲综合色| 啦啦啦中文免费视频观看日本| 春色校园在线视频观看| 最近的中文字幕免费完整| 男人添女人高潮全过程视频| 亚洲av日韩在线播放| 777米奇影视久久| 亚洲国产精品成人久久小说| 日韩视频在线欧美| 熟女av电影| 蜜臀久久99精品久久宅男| 看非洲黑人一级黄片| 少妇裸体淫交视频免费看高清| 亚洲精品中文字幕在线视频 | 国产大屁股一区二区在线视频| 最近中文字幕2019免费版| 中国三级夫妇交换| 精品久久久久久久末码| 高清毛片免费看| 欧美成人a在线观看| 国产v大片淫在线免费观看| 国产欧美亚洲国产| 亚洲av.av天堂| 男人爽女人下面视频在线观看| 午夜免费观看性视频| 欧美日韩在线观看h| 中国国产av一级| 国产精品久久久久久久久免| 成人高潮视频无遮挡免费网站| av国产免费在线观看| 男男h啪啪无遮挡| 久久久久久久亚洲中文字幕| 22中文网久久字幕| av国产精品久久久久影院| 久久精品国产a三级三级三级| 日韩,欧美,国产一区二区三区| 日本色播在线视频| 美女福利国产在线 | 亚洲精品一二三| 免费高清在线观看视频在线观看| 久久综合国产亚洲精品| 国产视频内射| 日本黄色日本黄色录像| 一区二区av电影网| 欧美日韩国产mv在线观看视频 | 日韩三级伦理在线观看| 国产精品久久久久成人av| 新久久久久国产一级毛片| 久久久久久久久久人人人人人人| 这个男人来自地球电影免费观看 | 亚洲最大成人中文| av在线观看视频网站免费| 天美传媒精品一区二区| 久久久久久久久久久丰满| 成年美女黄网站色视频大全免费 | 国产精品99久久久久久久久| 久久久成人免费电影| 日本一二三区视频观看| 人人妻人人看人人澡| 日本与韩国留学比较| 在线观看国产h片| 国产精品久久久久久久久免| 蜜臀久久99精品久久宅男| 成年人午夜在线观看视频| 欧美最新免费一区二区三区| 国产成人a∨麻豆精品| 国产伦理片在线播放av一区| 国产视频首页在线观看| 国产欧美日韩精品一区二区| 五月玫瑰六月丁香| 国产大屁股一区二区在线视频| 男的添女的下面高潮视频| 婷婷色综合www| 三级经典国产精品| 亚洲国产精品一区三区| kizo精华| 国国产精品蜜臀av免费| 国产伦精品一区二区三区四那| 亚洲精品乱久久久久久| 老女人水多毛片| 成人国产麻豆网| 婷婷色av中文字幕| 亚洲精品久久久久久婷婷小说| 男人爽女人下面视频在线观看| 精品国产露脸久久av麻豆| 国产成人免费无遮挡视频| 亚洲性久久影院| 精品一区在线观看国产| 精品一区二区三卡| 亚洲精品色激情综合| 国产91av在线免费观看| 日韩中字成人| 久久久久久久国产电影| 精华霜和精华液先用哪个| 三级国产精品片| 国产女主播在线喷水免费视频网站| 午夜免费鲁丝| 91狼人影院| 多毛熟女@视频| 国产一区有黄有色的免费视频| 3wmmmm亚洲av在线观看| 纯流量卡能插随身wifi吗| 国产精品偷伦视频观看了| 少妇人妻精品综合一区二区| 亚洲精品自拍成人| 三级国产精品片| 午夜福利在线在线| 啦啦啦啦在线视频资源| 街头女战士在线观看网站| 国产视频内射| 久久国产乱子免费精品| 蜜桃亚洲精品一区二区三区| 久久精品久久久久久噜噜老黄| 亚洲av综合色区一区| 亚洲美女黄色视频免费看| 韩国av在线不卡| 日日啪夜夜撸| 免费看av在线观看网站| 少妇人妻一区二区三区视频| 国产精品久久久久久久久免| 亚洲欧美成人综合另类久久久| 成年av动漫网址| 国产一区二区在线观看日韩| 久久精品国产亚洲av天美| 搡女人真爽免费视频火全软件| 欧美成人午夜免费资源| 99久久中文字幕三级久久日本| 精品亚洲成国产av| 国产亚洲91精品色在线| 男女无遮挡免费网站观看| 久久久国产一区二区| 久久精品熟女亚洲av麻豆精品| 五月玫瑰六月丁香| 人妻 亚洲 视频| 一级a做视频免费观看| 99久久精品一区二区三区| 男女边吃奶边做爰视频| 国产精品国产av在线观看| 国产精品一区二区在线不卡| 日韩成人伦理影院| 色视频在线一区二区三区| 内射极品少妇av片p| 日本av手机在线免费观看| 看免费成人av毛片| 亚洲欧美日韩卡通动漫| 99精国产麻豆久久婷婷| 亚洲精品日本国产第一区| 永久网站在线| 人妻少妇偷人精品九色| 亚洲精品自拍成人| 美女视频免费永久观看网站| 99热国产这里只有精品6| 亚洲不卡免费看| 色婷婷av一区二区三区视频| 成人美女网站在线观看视频| 欧美国产精品一级二级三级 | 三级国产精品欧美在线观看| 久久久久国产精品人妻一区二区| 欧美日韩在线观看h| 免费黄色在线免费观看| 亚洲精品日韩av片在线观看| 亚洲图色成人| 丰满人妻一区二区三区视频av| 中文字幕人妻熟人妻熟丝袜美| 日韩三级伦理在线观看| 成人午夜精彩视频在线观看| 我要看日韩黄色一级片| 岛国毛片在线播放| 午夜福利影视在线免费观看| 男的添女的下面高潮视频| 亚洲精品成人av观看孕妇| 日产精品乱码卡一卡2卡三| 99热这里只有是精品在线观看| 成人黄色视频免费在线看| 国产精品久久久久久久久免| 亚洲综合色惰| 亚洲丝袜综合中文字幕| 国产综合精华液| 少妇高潮的动态图| 夜夜骑夜夜射夜夜干| 亚洲精品aⅴ在线观看| 午夜激情福利司机影院| 五月伊人婷婷丁香| 97在线人人人人妻| 国产男女内射视频| 亚洲精品一二三| 嫩草影院新地址| 国产成人精品福利久久| 高清欧美精品videossex| 91久久精品电影网| 三级国产精品片| 最黄视频免费看| 新久久久久国产一级毛片| 在线观看三级黄色| 久久亚洲国产成人精品v| av国产精品久久久久影院| 波野结衣二区三区在线| 国产片特级美女逼逼视频| 国产黄色免费在线视频| 91精品国产九色| 精品一区二区免费观看| 成年女人在线观看亚洲视频| 天堂8中文在线网| 岛国毛片在线播放| av不卡在线播放| 亚洲精品视频女| 久热久热在线精品观看| 99久久中文字幕三级久久日本| 国产淫语在线视频| 视频中文字幕在线观看| 精品国产一区二区三区久久久樱花 | 中文乱码字字幕精品一区二区三区| 老司机影院毛片| 91久久精品国产一区二区成人| 丝袜喷水一区| av免费观看日本| 三级国产精品片| 欧美精品人与动牲交sv欧美| 最近手机中文字幕大全| 九草在线视频观看| 中国国产av一级| 久久毛片免费看一区二区三区| 一级片'在线观看视频| 乱码一卡2卡4卡精品| 国产伦精品一区二区三区视频9| 亚洲av综合色区一区| 久久热精品热| 99久久综合免费| 五月开心婷婷网| 亚洲色图综合在线观看| 亚洲丝袜综合中文字幕| 国产成人精品婷婷| 久久久久久久亚洲中文字幕| 菩萨蛮人人尽说江南好唐韦庄| 精品一区二区三区视频在线| 欧美日韩综合久久久久久| 色婷婷久久久亚洲欧美| 22中文网久久字幕| 成年免费大片在线观看| 搡女人真爽免费视频火全软件| 久久国产精品男人的天堂亚洲 | 91狼人影院| 好男人视频免费观看在线| 欧美精品一区二区大全| 妹子高潮喷水视频| 色吧在线观看| 国产伦精品一区二区三区四那| a级毛片免费高清观看在线播放| 欧美激情极品国产一区二区三区 | 日韩欧美 国产精品| 干丝袜人妻中文字幕| 日日撸夜夜添| 在线播放无遮挡| 高清不卡的av网站| 制服丝袜香蕉在线| 超碰97精品在线观看| 又大又黄又爽视频免费| 亚洲第一av免费看| 午夜免费鲁丝| 成年美女黄网站色视频大全免费 | a 毛片基地| 国产黄色免费在线视频| 久久精品国产a三级三级三级| 日本猛色少妇xxxxx猛交久久| 日本黄色日本黄色录像| 国产精品嫩草影院av在线观看| 国产精品成人在线| 一个人免费看片子| 在线观看国产h片| 国产男女超爽视频在线观看| 精品人妻熟女av久视频| 最黄视频免费看| 国产色婷婷99| 91在线精品国自产拍蜜月| 国产精品久久久久久久电影| 国产亚洲一区二区精品| 国产成人91sexporn| 免费看不卡的av| av在线播放精品| 国产免费一级a男人的天堂| 欧美丝袜亚洲另类| 男女下面进入的视频免费午夜| 国产免费又黄又爽又色| 久久青草综合色| 我的老师免费观看完整版| 香蕉精品网在线| 十分钟在线观看高清视频www | 九草在线视频观看| 久久久久精品性色| 免费播放大片免费观看视频在线观看| 99久久精品一区二区三区| 在线观看免费高清a一片| 国产爱豆传媒在线观看| 国产深夜福利视频在线观看| 国产亚洲最大av| 人人妻人人澡人人爽人人夜夜| 狂野欧美白嫩少妇大欣赏| 亚洲怡红院男人天堂| 另类亚洲欧美激情| 能在线免费看毛片的网站| av在线观看视频网站免费| 老师上课跳d突然被开到最大视频| 伦理电影免费视频| 国产精品一区二区在线观看99| 午夜激情福利司机影院| 一级毛片 在线播放| 人妻一区二区av| 午夜福利影视在线免费观看| 欧美日韩在线观看h| 精品久久久噜噜| 一区二区三区四区激情视频| 久久久久久九九精品二区国产| 免费黄网站久久成人精品| av一本久久久久| 欧美日韩综合久久久久久| 国产亚洲一区二区精品| 国产精品人妻久久久影院| 国产在线视频一区二区| 国产精品久久久久久精品电影小说 | 狂野欧美激情性bbbbbb| 亚洲伊人久久精品综合| 久久av网站| 男人和女人高潮做爰伦理| 国产一区二区在线观看日韩| 80岁老熟妇乱子伦牲交| 能在线免费看毛片的网站| 亚洲欧美精品专区久久| 黄色欧美视频在线观看| 少妇猛男粗大的猛烈进出视频| 亚洲一区二区三区欧美精品| 老师上课跳d突然被开到最大视频| 天堂8中文在线网| 永久免费av网站大全| 中文字幕免费在线视频6| 亚洲精品国产成人久久av| 九九在线视频观看精品| 国产一级毛片在线| 亚洲国产欧美人成| 精品国产一区二区三区久久久樱花 | 国产伦理片在线播放av一区| av不卡在线播放| 久久精品久久久久久噜噜老黄| 另类亚洲欧美激情| 99久久精品国产国产毛片| 最黄视频免费看| 久久久欧美国产精品| 免费观看的影片在线观看| 精品国产乱码久久久久久小说| 午夜免费男女啪啪视频观看| 美女cb高潮喷水在线观看| 亚洲熟女精品中文字幕| 99久久精品一区二区三区| 最后的刺客免费高清国语| 一区在线观看完整版| 午夜视频国产福利| 夜夜骑夜夜射夜夜干| 日韩伦理黄色片| 91精品一卡2卡3卡4卡| 在线 av 中文字幕| 日韩制服骚丝袜av| 99国产精品免费福利视频| 国产成人aa在线观看| 国产一区二区在线观看日韩| av在线app专区| 伦理电影免费视频| 五月开心婷婷网| 水蜜桃什么品种好| 日韩伦理黄色片| 国产欧美日韩精品一区二区| 亚洲天堂av无毛| 少妇的逼好多水|