NPP
(Synonyms: N-(2-苯乙基)-4-哌啶酮) 目录号 : GC40748
An Analytical Reference Standard
Cas No.:39742-60-4
Sample solution is provided at 25 µL, 10mM.
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Quality Control & SDS
- View current batch:
- Purity: >98.00%
- COA (Certificate Of Analysis)
- SDS (Safety Data Sheet)
- Datasheet
NPP is an analytical reference standard that is categorized as a piperidinone. It is a precursor in the synthesis of fentanyl and related opioids. NPP may also be found as an impurity in fentanyl preparations. The physiological and toxicological properties of this compound are not known. This product is intended for research and forensic applications.
| Cas No. | 39742-60-4 | SDF | |
| 别名 | N-(2-苯乙基)-4-哌啶酮 | ||
| Canonical SMILES | O=C1CCN(CCC2=CC=CC=C2)CC1 | ||
| 分子式 | C13H17NO | 分子量 | 203.3 |
| 溶解度 | DMF: 30 mg/ml,DMSO: 100 mg/ml,Ethanol: 100 mg/ml | 储存条件 | Store at -20°C |
| General tips | 请根据产品在不同溶剂中的溶解度选择合适的溶剂配制储备液；一旦配成溶液，请分装保存，避免反复冻融造成的产品失效。 储备液的保存方式和期限：-80°C 储存时，请在 6 个月内使用，-20°C 储存时，请在 1 个月内使用。 为了提高溶解度，请将管子加热至37℃，然后在超声波浴中震荡一段时间。 |
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| Shipping Condition | 评估样品解决方案：配备蓝冰进行发货。所有其他可用尺寸：配备RT，或根据请求配备蓝冰。 | ||
| 制备储备液 | |||
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1 mg | 5 mg | 10 mg |
| 1 mM | 4.9188 mL | 24.5942 mL | 49.1884 mL |
| 5 mM | 0.9838 mL | 4.9188 mL | 9.8377 mL |
| 10 mM | 0.4919 mL | 2.4594 mL | 4.9188 mL |
| 第一步：请输入基本实验信息（考虑到实验过程中的损耗，建议多配一只动物的药量） | ||||||||||
| 给药剂量 | mg/kg |  |
动物平均体重 | g | |
每只动物给药体积 | ul | |
动物数量 | 只 |
| 第二步：请输入动物体内配方组成（配方适用于不溶于水的药物；不同批次药物配方比例不同，请联系GLPBIO为您提供正确的澄清溶液配方） | ||||||||||
| % DMSO % % Tween 80 % saline | ||||||||||
| 计算重置 | ||||||||||
计算结果:
工作液浓度： mg/ml；
DMSO母液配制方法： mg 药物溶于 μL DMSO溶液（母液浓度 mg/mL，
体内配方配制方法：取 μL DMSO母液，加入 μL PEG300，混匀澄清后加入μL Tween 80，混匀澄清后加入 μL saline，混匀澄清。
1. 首先保证母液是澄清的；
2.
一定要按照顺序依次将溶剂加入，进行下一步操作之前必须保证上一步操作得到的是澄清的溶液，可采用涡旋、超声或水浴加热等物理方法助溶。
3. 以上所有助溶剂都可在 GlpBio 网站选购。
Is NPP proportional to GPP? Waring's hypothesis 20 years on
Tree Physiol 2019 Aug 1;39(8):1473-1483.PMID:30924876DOI:10.1093/treephys/tpz034.
Gross primary production (GPP) is partitioned to autotrophic respiration (Ra) and net primary production (NPP), the latter being used to build plant tissues and synthesize non-structural and secondary compounds. Waring et al. (1998; Net primary production of forests: a constant fraction of gross primary production? Tree Physiol 18:129-134) suggested that a NPP:GPP ratio of 0.47 ± 0.04 (SD) is universal across biomes, tree species and stand ages. Representing NPP in models as a fixed fraction of GPP, they argued, would be both simpler and more accurate than trying to simulate Ra mechanistically. This paper reviews progress in understanding the NPP:GPP ratio in forests during the 20 years since the Waring et al. paper. Research has confirmed the existence of pervasive acclimation mechanisms that tend to stabilize the NPP:GPP ratio and indicates that Ra should not be modelled independently of GPP. Nonetheless, studies indicate that the value of this ratio is influenced by environmental factors, stand age and management. The average NPP:GPP ratio in over 200 studies, representing different biomes, species and forest stand ages, was found to be 0.46, consistent with the central value that Waring et al. proposed but with a much larger standard deviation (±0.12) and a total range (0.22-0.79) that is too large to be disregarded.
Spatiotemporal distribution of the atmospheric 14C around Ningde NPP
J Environ Radioact 2022 Oct;251-252:106958.PMID:35797904DOI:10.1016/j.jenvrad.2022.106958.
In this paper, the sampling and monitoring methods of atmospheric 14C around Ningde NPP were presented, and the variations and trends during 2013-2021 were statistically analyzed and comparatively studied with worldwide reported values around NPPs. Meanwhile, the correlation study with the gaseous effluent emission amount from Ningde NPP was analyzed, and the spatial distribution of the atmospheric 14C around Ningde NPP was simulated with the atmospheric release based on the long-term meteorological parameters with the plume diffusion model. It was shown that the average specific activity of atmospheric 14C at each sampling site ranged from 229 to 230 mBq/gC, and the weak evidence of influence on the nearest sampling site from the release of the NPP could be observed. Seasonal variations of 14C specific activity were analyzed, and it was shown that, except for the site 1.7 km from the NPP, the specific activity of the atmospheric 14C was higher in summer and autumn and lower in winter and spring. Besides, it was shown that the excess 14C for long-term monitoring results around the NPP was consistent with the simulated values on the order of magnitude.
Evaluation of NPP using three models compared with MODIS-NPP data over China
PLoS One 2021 Nov 18;16(11):e0252149.PMID:34793471DOI:10.1371/journal.pone.0252149.
Estimating net primary productivity (NPP) is significant in global climate change research and carbon cycle. However, there are many uncertainties in different NPP modeling results and the process of NPP is challenging to model on the absence of data. In this study, we used meteorological data as input to simulate vegetation NPP through climate-based model, synthetic model and CASA model. Then, the results from three models were compared with MODIS NPP and observed data over China from 2000 to 2015. The statistics evaluation metrics (Relative Bias (RB), Pearson linear Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE)) between simulated NPP and MODIS NPP were calculated. The results implied that the CASA-model performed better than the other two models in terms of RB, RMSE, NSE and CC whether on the national or the regional scale. It has a higher CC with 0.51 and a smaller RMSE with 111.96 g C·m-2·yr-1 in the whole country. The synthetic model and CASA-model has the same advantages at some regions, and there are lower RMSE in Southern China (86.35 g C·m-2·yr-1), Xinjiang (85.53 g C·m-2·yr-1) and Qinghai-Tibet Plateau (93.22 g C·m-2·yr-1). The climate-based model has widespread overestimation and large systematic errors, along with worse performances (NSEmax = 0.45) and other metric indexes unsatisfactory, especially Qinghai-Tibet Plateau with relatively lower accuracy because of the unavailable observation data. Overall, the CASA-model is much more ideal for estimating NPP all over China in the absence of data. This study provides a comprehensive intercomparison of different NPP-simulated models and can provide powerful help for researchers to select the appropriate NPP evaluation model.
Climate change leads to higher NPP at the end of the century in the Antarctic Tundra: Response patterns through the lens of lichens
Sci Total Environ 2022 Aug 20;835:155495.PMID:35472357DOI:10.1016/j.scitotenv.2022.155495.
Poikilohydric autotrophs are the main colonizers of the permanent ice-free areas in the Antarctic tundra biome. Global climate warming and the small human footprint in this ecosystem make it especially vulnerable to abrupt changes. Elucidating the effects of climate change on the Antarctic ecosystem is challenging because it mainly comprises poikilohydric species, which are greatly influenced by microtopographic factors. In the present study, we investigated the potential effects of climate change on the metabolic activity and net primary photosynthesis (NPP) in the widespread lichen species Usnea aurantiaco-atra. Long-term monitoring of chlorophyll a fluorescence in the field was combined with photosynthetic performance measurements in laboratory experiments in order to establish the daily response patterns under biotic and abiotic factors at micro- and macro-scales. Our findings suggest that macroclimate is a poor predictor of NPP, thereby indicating that microclimate is the main driver due to the strong effects of microtopographic factors on cryptogams. Metabolic activity is also crucial for estimating the NPP, which is highly dependent on the type, distribution, and duration of the hydration sources available throughout the year. Under RCP 4.5 and RCP 8.5, metabolic activity will increase slightly compared with that at present due to the increased precipitation events predicted in MIROC5. Temperature is highlighted as the main driver for NPP projections, and thus climate warming will lead to an average increase in NPP of 167-171% at the end of the century. However, small changes in other drivers such as light and relative humidity may strongly modify the metabolic activity patterns of poikilohydric autotrophs, and thus their NPP. Species with similar physiological response ranges to the species investigated in the present study are expected to behave in a similar manner provided that liquid water is available.
Does climate directly influence NPP globally?
Glob Chang Biol 2016 Jan;22(1):12-24.PMID:26442433DOI:10.1111/gcb.13079.
The need for rigorous analyses of climate impacts has never been more crucial. Current textbooks state that climate directly influences ecosystem annual net primary productivity (NPP), emphasizing the urgent need to monitor the impacts of climate change. A recent paper challenged this consensus, arguing, based on an analysis of NPP for 1247 woody plant communities across global climate gradients, that temperature and precipitation have negligible direct effects on NPP and only perhaps have indirect effects by constraining total stand biomass (Mtot ) and stand age (a). The authors of that study concluded that the length of the growing season (lgs ) might have a minor influence on NPP, an effect they considered not to be directly related to climate. In this article, we describe flaws that affected that study's conclusions and present novel analyses to disentangle the effects of stand variables and climate in determining NPP. We re-analyzed the same database to partition the direct and indirect effects of climate on NPP, using three approaches: maximum-likelihood model selection, independent-effects analysis, and structural equation modeling. These new analyses showed that about half of the global variation in NPP could be explained by Mtot combined with climate variables and supported strong and direct influences of climate independently of Mtot , both for NPP and for net biomass change averaged across the known lifetime of the stands (ABC = average biomass change). We show that lgs is an important climate variable, intrinsically correlated with, and contributing to mean annual temperature and precipitation (Tann and Pann ), all important climatic drivers of NPP. Our analyses provide guidance for statistical and mechanistic analyses of climate drivers of ecosystem processes for predictive modeling and provide novel evidence supporting the strong, direct role of climate in determining vegetation productivity at the global scale.