AIR-Chem: an Authentic Intelligent Robotics for Chemistry - The

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A: New Tools and Methods in Experiment and Theory

AIR-Chem: an Authentic Intelligent Robotics for Chemistry Jiagen Li, Yihua Lu, Yanheng Xu, Chongfeng Liu, Yuxiao Tu, Shuqian Ye, Haochen Liu, Yi Xie, Huihuan Qian, and Xi Zhu J. Phys. Chem. A, Just Accepted Manuscript • DOI: 10.1021/acs.jpca.8b10680 • Publication Date (Web): 05 Nov 2018 Downloaded from http://pubs.acs.org on November 8, 2018

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is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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The Journal of Physical Chemistry

AIR-Chem: an Authentic Intelligent Robotics for Chemistry

Jiagen Li1, Yihua Lu1, Yanheng Xu1, Chongfeng Liu1, Yuxiao Tu1, Shuqian Ye1, Haochen Liu2, Yi Xie3*, Huihuan Qian1,4*, Xi Zhu1,4* 1. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen. Shenzhen, Guangdong, 518172, China 2. Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China 3. Hefei

National

Laboratory

for

Physical

Sciences

at

Microscale,

Collaborative Innovation Center of Chemistry for Energy Materials, University of Science and Technology of China, Hefei, Anhui 230026, China. 4. Robotics and Artificial Intelligence Lab, The Chinese University of Hong Kong, Shenzhen. Shenzhen, Guangdong, 518172, China Corresponding Authors: Yi Xie: [email protected], Huihuan Qian: [email protected], Xi Zhu: [email protected]

ACS Paragon Plus Environment

The Journal of Physical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT: The new era with prosperous artificial intelligence (AI) and robotics technology is reshaping the materials discovery progress in a more radical fashion. Here we present an Authentic Intelligent Robotics for Chemistry (AIR-Chem), integrated with technological innovations in AI and robotics fields, functionalized with modules includes gradient descent based optimization frameworks, multiple external field modulations, real-time computer vision (CV) system and automated guided vehicle (AGV) parts. AIR-Chem is portable and remotely controllable by cloud computing. The AIR-Chem can learn the parametric procedures towards the given targets and carry on the laboratory operations standalone, with high reproductivity, precision, and availability for knowledge regeneration. Moreover, an improved nucleation theory of size focusing on inorganic perovskite quantum dots (IPQDs) is theoretically proposed and experimentally testified by AIR-Chem. This work aims to boost the process of unmanned chemistry lab from synthesis of chemical materials to the analysis of physical chemical property, it provides a vivid demo for the future chemistry reshaped by AI and robotics technology. TOC GRAPHICS

KEYWORDS: perovskite quantum dots, automatic synthesis, in-situ computer


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vision, size monitoring, parameter self-adjusting, size focusing theory.

Recently, the robotics technology penetrates the explosive growth field of the autonomous chemical laboratory, especially in nanomaterials synthesis1-4. In 2010, Chan group described an automated platform for the high throughput, high reproducibility synthesis of colloidal nanocrystals and optimization of physical properties relevant to emerging applications of nanomaterials2. Peng et al demonstrated an improved autonomous system coupled with liquid-phase Fourier transform infrared (FTIR) and UV–vis measurements applied for monodisperse CdS nanocrystals synthesis5. In industrial sectors, Merck3 and Pfizer4 respectively developed their highthroughput screening system based on flow chemistry for medical discovery in early 2018, the power of robots on automatic synthesis efficiency was strongly addressed in those works. Furthermore, combined with CV technology, a robotic system which can search exfoliated two-dimensional crystals and assembles them into superlattices with high efficiency and low error rate was developed by Masubuchi group6. Since then, the combination of robotic automation and AI has made great achievement in chemistry on improving yields and reproducibility, cutting cost, preventing the health hazard, accelerating the discovery of new materials eventually. Here in this work we made a well-integrated intelligent chemistry robotics systems AIR-Chem, with portable type modules, which can take over typical nanoscience experiment, with improved sample quality, operation efficiency, random bug free, portable and remote controllable, and more importantly, the embedded CV system can capture the short time scale experimental data. The parameter optimization module also



supports the self-adjusted reactants dose design for the targets with the expected property.

Robot design AIR-Chem consists of two parts: a multi-robot system and an intelligent operation platform. As the hardware part, the multi-robot system (schematically shown in Figure 1(a) and the real machines shown in Figure 1(b)) includes a robot arm-AGV system for object seeking and transferring, a reaction chamber-AGV system for reaction conditions providing, and a workstation for reagent and vessel preparation. Detailed information on robot design and control is presented in supplement 1.1~1.3. Work flowchart of the automatic synthesis process is presented in supplementary 1.4. Fig S8.


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Figure 1. (a)The schematics of the detail of AIR-Chem and the functionalities of a critical part. The presented AIR-Chem, it includes a prepare workstation with CV system, a robot arm, and a reaction chamber. The robot arm and the reaction chamber are placed on AGV with automatically navigation under the help of radar. (b), Photographs of the entire system. Reaction chamber and 4-axis robot arm are placed on AGV, and all controlled by Raspberry Pi. A peristaltic pump for liquid injection is installed at the back of the reaction chamber, see the insert photo at top right. Reference supplement 1.1 to 1.3 for the detail configuration of each part. The insert picture at top left shows a clear view of the user interfaces for PC user.

Different from the previous on-site laboratory device, the robot arm, and the reaction chamber are separated and freely movable due to the AGV model, it provides one more additional variable for the experimental condition. The robot arm can prepare the samples in one condition (like vacuum, high temperature, high humidity, high light irradiation etc) and the reaction in the chamber can go on in other conditions, or



suddenly change to other ones by transporting the reaction chamber to another room with totally different humidity, bacterial concentration, super high/cold temperature, even different gravitational acceleration etc. These conditions changing scheme provides more opportunities to explore more chemical events7,8. Mapping of laboratory and path navigation for AGV is shown in supplement 2.2.

Real-time CV characterization and self-adjusted synthesis of IPQDs The research of IPQDs, like CsPbBr3, is one of the major spotlights because of the remarkably high photovoltaic (PV) efficiency.9 During the synthesis process of CsPbBr3 QDs, the size focusing of quantum dots and the saturated capacity of well dispersed QDs in toluene are essential for the quality control. The size information and the saturated disperse capacity of QDs can be captured by the Photoluminescence (PL) peak and intensity respectively, associated with the quantum efficient theory2. However, traditional in-situ spectrographic instruments are expensive due to the high resolution spectrometer for wavelength splitting. In this work, as the fact that QDs materials are ideal discrete RGB-emitting materials for display technology10, thus the CV technology can perfectly bridge the color information with the optoelectronic properties due to the strong correlation between the two parts. Here we will show that this low-cost technology performs well for nanocrystal synthesis monitoring.

In AIR-Chem system, an in-situ characterization system coupled with UV light renovated upon OpenCV library of CV technology 11 was developed to monitoring the


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process of chemical synthesis. Ultra-high pixel resolution industrial cameral was equipped inside the reaction chamber for the optical characterization. AIR-Chem records the video of CsPbBr3 QDs synthesized under 365nm UV illumination for the whole process. The data analysis process is shown in Figure 2a. The video stream was divided into two color modes-RGB mode (Figure 2b) and grey mode (Figure 2c). Data in grey mode was utilized to obtain the positions of the solution in beaker based on edge detection as shown in Figure 2d. To avoid the color variation noise caused by the fluctuation of the solution, regions of the uniform grey level will be selected automatically as shown in the red area in Figure 2e. Frames of selected regions will be analyzed in RGB mode after Gaussian and mean filter. This multiple color mode and flux analysis system embedded in AIR-Chem upgrades from the former OpenCV mode scheme applicated on food science and chemical analysis12-15.

In the cesium lead halide perovskites structures, the emitted RGB color information can be exactly mapped with the QDs size16, and by tuning the halides types and weights, the entire visible light ranging from 410–700 nm can be achieved16,17. In AIR-Chem, the RGB color bar can automatically be converted to the energy bar or the emitting wavelength pattern18 in the RGB QDs system (supplement 2.3), which directly bridge the pixel space with the energy space in a straightforward visualization way. By tuning the component and weights of halides in cesium lead halide perovskite structures, the entire visible light ranges from 410–700 nm can be achieved16,17. Since that in IPQDs, the band gap and RGB pixel color are a bijection. Specifically, for the CsPbBr3-xIx



(0 < x < 3) mixed phase, the band gap is increasing with x from x=0 (CsPbBr3) towards x=3 (CsPbI3) 19. Hereby the color inspired precisely amount control system by optimization scheme, AIR-Chem can automatically prepare the solution with given RGB color information by adjusting the dosage of all the solid and liquid reactants (supplement 2.4). While during the process, AIR-Chem records all the RGB information, since the RGB are primary colors, all the other colors can be divided into the linear combination of RGB bases, especially, in the IPQDs, the R and G represent the CsPbBr3 and CsPbBr1I2 structures respectively. Here for the CsPbBr3-xIx mixed halide structures, the random forest scheme in AIR-Chem here maps the RGB color information with the x value and the x dependent band gap information directly. As shown in Figure 2g, AIR-Chem first conducted 9 experiments following x from 0 to 2, the converged R/G (there is no blue information here) ratio is recorded for each sample around 300s, an example is shown in Figure 2f (x=1.5), which indicates 𝑅 𝐺 = 2. By comparing the x dependent onsite absorption peak and the R/G ratio, a linear correlation between the R/G ratio and the band gap derived from RGB (𝐸𝑅𝐺𝐵 𝑔 ), as shown in the inset in Figure 2g, 𝐸𝑅𝐺𝐵 can described as: 𝑔 𝐶𝑠𝑃𝑏𝐵𝑟1𝐼2

3 = 𝐺% * 𝐸𝐶𝑠𝑃𝑏𝐵𝑟 +𝑅% * 𝐸𝑔 𝐸𝑅𝐺𝐵 𝑔 𝑔

eq (1)

Since B component is almost 0, (𝐺 + 𝑅)% = 1, 𝐸𝑅𝐺𝐵 is only 𝑅 𝐺 dependent. The 𝑔 𝑅𝐺𝐵 16 reported band gap 𝐸𝐸𝑥𝑝 𝑔 in previous works are plotted in the square, we can see 𝐸𝑔

agrees well with 𝐸𝐸𝑥𝑝 𝑔 , in the system where the colors are continually distributed between two ends, like CsPbBr3-xIx, the CV technology equipped AIR-Chem can read, the band gap value and I/Br ratio from the RGB information directly. Moreover, we can


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see in Figure 2f that the red part (I rich) grows fast than the green one (Br rich), since the growth of the QDs are usually diffusion limited, the bond energy of Pb-I (194.5 kJ/mol20) is weaker than that of Pb-Br (248.5 kJ/mol20), indicating the high diffusion coefficient. According to the above results, self-adjusted parameter modification experiments were further taken. Based on gradient decent optimization algorithms and the database in figure 2g, CsPbBr3-xIx QDs with target bandgap 2.10eV were produced with energy error below 0.01eV after 8 times experiments, shown in Figure 2h. Implementation details of the automatic synthesis process, characterization results and parameter adjusting algorithms are in supplement 2.4.

Figure 2. The mapping of the RGB color information and the band gap. a, Schematic of processing algorithm for grabbing visual information for in-situ analysis. b to e, Representative images of the reaction captured by embedded CV system in AIR-Chem at each step. The region around by green dash line in d and e is detected as an effective region, and a red square indicates in e is an optimal stable area for analysis. f, The real-



time RGB value during the QDs synthesis. g, Mapping results with the ratio 𝑅 (𝑅 + 𝐺) (pixie space), black square indicate the experiment result of 𝐸𝑔 in this work, the red line is the linear fitting for that, blue circle shows the ratio of I and Br in reactant. h, Parameter adjusting process of target emission bandgap 2.10eV QDs synthesis

In-situ monitoring of size growth in CsPbBr3 QDs synthesis The RGB color information contains more than the band gap, the curvature of the RGB curve is the direct reflection of the growth rate of the QDs. The quantum confinement effect dominates the optical properties in QDs21, the average QDs size 𝐷 can be calculated directly from the onsite absorption band gap16, which is strongly correlated with the emitting color. Thus, more than the band gap related emitting color, the curvature of the RGB curve reflects the growth rate and the size expansion of the QDs as well. Since the RGB data has been convert to band gap information, the average QDs size 𝐷 can be derived spontaneously in AIR-Chem (supplement 2.5). As shown in Figure 3. The saturated CsPbBr3 QDs solution synthesized by AIR-Chem is transparently green, well benchmarked with the previous laboratory data16, with RGB value (0,175,25). While in the initial and final stage, there are partially incidental blue and red components associated with the major green, indicating the significant quantum confinement size effects. The most significant data provided by AIR-Chem is the growth rate of 𝐷, as shown in the insert picture in Figure 3. Apparently, the physics picture agrees well with the theory of Clark et al22, indicating the growth type of CsPbBr3 QDs is of the diffusion-limited catalogue. The time dependent 𝐷 derived is converged around 12 nm, which agrees well with the TEM images and PL peaks


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(511nm) as shown in Figure S13. The consentaneous match between the OpenCV derived data and the theoretic kinetic model as well as the post-processing characterization demonstrates the validity and robustness of the analytical ability of AIR-Chem.

D

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Figure 3. Real-time RGB color map of the CsPbBr3 QDs with the average diameter 𝐷. The R, G, B colors are in red, green and blue respectively. The insert figure shows the real time average size 𝐷 in nm. The model of Clark et al22 is embedded as well for benchmark.

Augmented size focusing theory for QDs Previous experimental 23and theoretic22 work manifested that the QDs size focusing can be well improved by additional injection. AIR-Chem can precisely set the periodical injection events by interval time and the liquid amount for each injection, through the liquid control modules as shown in Figure 4a. We set the injection every 4 min



periodically. For the time domain, as shown in Figure 4b, we can see for the monoinjection case, the growth rate of 𝐷 drops quickly after the first 50 seconds, in the long 𝐷

time range, 𝑅 (𝑅 = 2 ) increases in the shape of the traditional LSW (Lifshitz-SlyozovWagner) theory, the growing rate can be defined as24,25 : 𝑑𝑅 𝑑𝑡

=

𝜐𝐷𝑑𝑖𝑓𝑓 𝑅

((𝑐 ― 𝑐

∞)

―

2𝛾𝑐∞ 𝑅𝑘𝑇

)

eq (2)

Where c is the solution monomer concentration, 𝛾 is the surface tension, 𝜐 is the molar volume, 𝐷𝑑𝑖𝑓𝑓 is the diffusivity of the monomer, 𝑐∞ is the monomer solubility. eq (2) indicates that growth rate 𝑑𝑅 𝑑𝑡 is proportional to the monomer concentration c. The value of c is 0.1μM/ml in the AIR-Chem experiments with temperature 300K, if we convert 𝑅 into the corresponding emitted wavelength, it takes about 400s for the spectrum measurements converges to 1nm, which is close to the resolution. If the synthesis methodology is in 170 ℃ with concentration 15μM/ml, as reported previously, c is about 150 times denser than the AIR-Chem sets, according to eq (1), for the growth rate, the temperature difference is negligible compared with the c contrast, the predictive converging time is about 400s/150≈2.7s, which agrees well with the literature (about 3s)16. Equipped with CV technology, the AIR-Chem can derive the real time analysis well benchmarked with image, spectrum and time information for the current CsPbBr3 QDs experiments, and extendable for more others.


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Figure 4. The general monomer production rate improved size focusing mechanism approached by precise injection rate control. (a) scheme for the injection rate control system and procedure. (b), the real-time diameter 𝐷 export from the AIR-Chem up to 3 times of injection, the Clark model is shown in black solid line. The pink and origin solid line reflect the 2nd and 3rd injection. The inserted picture (c) shows the size distribution with variable times of precursors injection. The red and blue line separately indicates 0.0025mmol and 0.05mmol well-mixed Cs+, Pb2+, 3Br- in 0.5ml precursors per single injection.

Next, we further introduce how AIR-Chem advance the size distribution issue in the CsPbBr3 QDs. The injection rate  of precursors is supposed to be a key parameter for high quality narrow sized QDs synthesis22,23, but whether  can be further optimized or not and how the process could be is still unknown either experimentally or theoretically. Other than the reaction or diffusion, the key point towards the understanding of the  determined size distribution picture lies on the monomers, which transformed from injected precursors, acting as crystallizable units for the crystal growth of QDs26,27. The



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introduction of monomer production rate effect was described in eq (2) from Clark’s work, the standard deviation () of the size distribution of QDs was quantitatively determined by eq (3). 𝜎(𝑡,𝜏) 𝜎0

(

=(

⟨𝑅⟩

1

⟨𝑅〉0)

𝜉(𝑡, 𝜏)

)

―1

eq (3) 𝑣𝑐 1

〈𝑅〉

where ξ is ‘size focusing coefficient’, defined as ξ = 4𝜋𝑍𝐾𝐷 = ( 𝑅𝑐 ―1), here 𝑐 is the injection ratio, Z is the number concentration. Practically, ξ was in the form ξ = 𝜉0 𝑒 ―𝑘𝑡 due to the 1st order precursor reaction.

AIR-Chem offers an excellent platform to investigate the  dependent , the isolated reaction chamber excludes the other error factors, 4 parallel experiments with various 𝑐 and injection times n (n = ⌊𝑡/𝑇⌋, T is the time interval of each injection) are conducted synchronous.  could be determined as τ = n𝑐. By analysis the statistic results of TEM , it obviously shows that  can increase by injection with some 𝑐 quantity, Clark’s theory22 is challenged. We take the injection events as time-dependent collective effects, ξ is replaced by 𝜉' 𝑣𝑐𝑖𝑒 ―𝑘𝑡(

(𝑛 , 𝑐(𝑡)) =

1 ― 𝑒(𝑛 + 1)𝑘T ) 1 ― 𝑒𝑇

1 𝐾𝐷

4𝜋𝑍 𝑒𝑛𝑘𝑇

𝜉

. Considering the collective effect, an effective ξ, 𝜉′ = 𝑌 𝑎

is updated, here Y = 1 ― 𝑒𝑘𝑇( ― 𝑒𝑘𝑇 + 𝑐 ). Where 𝑎 is a constant (details are in the supplement 3). It is noteworthy that 𝑐 has a critical value 𝑐0 = (𝑎𝑒𝑘𝑇) , when 𝑐 < 𝑐0 (refers to small-volume mono time injection), Y is negative, 𝜎 increases with n, the defocusing occurs, vice versa. When 𝑐 > 𝑐0, (refers to large-volume mono time injection), the size will keep focusing with every additional injection. The numerical results are plotted in Figure 4c for the solid blue and red lines, Peng’s23 and Clark’s22


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work is a special case for our general description where n = 0 and 𝑎 = 0, which only can produce mono branch as plotted in the solid black line in Figure 4c. The best solution for optimizing the size distribution issue is to increase the injection time with moderate amounts per time. More experiments data supports the new proposed nucleation theory. This further confirms that multi-times fast injection could benefits the size focusing in nanocrystal growth. The precise experimental condition control and the robust operation performance of the AIR-Chem help the discovery in the theoretical domain, the intelligent robotics can be preeminent quality assurance for the approximation made in chemical theories and beyond.

To conclude, in this work we provided an intelligent robotics for chemistry laboratory—AIR-Chem, which can well take over typical chemical experiments in nanoscience and technology with human activity free. It can couple multiple external fields (electric field, temperature gradient, light field etc) simultaneously as a nonadiabatic perturbation for both the thermodynamics and kinetics events. By utilizing gradient descent based optimization algorithms, AIR-Chem is capable of self-adjust for the input parameters for the given targets. The embedded real-time CV system integrated with PL device benefits the study of nanocrystal growth the new nucleation theory has been derived. The AIR-Chem provides a vivid demo for the intelligent laboratory robotics.

ACKNOWLEDGMENT This work is supported by Robotic Discipline Development Fund (2016-1418) from



Shenzhen

Government,

Shenzhen

Fundamental

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Research

foundation

(JCYJ20170818103918295), National Natural Science Foundation of China (Grant No 21805234) and President’s funds from CUHK-Shenzhen (PF00728).

REFERENCES (1)

Granda, J. M.; Donina, L.; Dragone, V.; Long, D.-L.; Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 2018, 559 (7714), 377.

(2)

Chan, E. M.; Xu, C.; Mao, A. W.; Han, G.; Owen, J. S.; Cohen, B. E.; Milliron, D. J. Reproducible, high-throughput synthesis of colloidal nanocrystals for optimization in multidimensional parameter space. Nano Lett 2010, 10 (5), 1874.

(3)

Gesmundo, N. J.; Sauvagnat, B.; Curran, P. J.; Richards, M. P.; Andrews, C. L.; Dandliker, P. J.; Cernak, T. Nanoscale synthesis and affinity ranking. Nature 2018, 557 (7704), 228.

(4)

Perera, D.; Tucker, J. W.; Brahmbhatt, S.; Helal, C. J.; Chong, A.; Farrell, W.; Richardson, P.; Sach, N. W. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science 2018, 359, 429.

(5)

Li, J.; Wang, H.; Lin, L.; Fang, Q.; Peng, X. Quantitative Identification of Basic Growth Channels for Formation of Monodisperse Nanocrystals. J Am Chem Soc 2018, 140 (16), 5474.

(6)

Masubuchi, S.; Morimoto, M.; Morikawa, S.; Onodera, M.; Asakawa, Y.; Watanabe, K.; Taniguchi, T.; Machida, T. Autonomous robotic searching and assembly of two-dimensional crystals to build van der Waals superlattices. Nat Commun 2018, 9 (1), 1413.

(7)

Zhou, Z.; Li, X.; Zare, R. N. Optimizing Chemical Reactions with Deep


Page 17 of 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Reinforcement Learning. ACS Central Science 2017, 3 (12), 1337. (8)

Caramelli, D.; Salley, D.; Henson, A.; Camarasa, G. A.; Sharabi, S.; Keenan, G.; Cronin, L. Networking chemical robots for reaction multitasking. Nature Communications 2018, 9 (1), 3406.

(9)

Szuromi, P.; Grocholski, B. Natural and engineered perovskites. Science 2017, 358 (6364), 732.

(10)

Kim, Y.-H.; Cho, H.; Lee, T.-W. Metal halide perovskite light emitters. Proceedings of the National Academy of Sciences 2016, 113 (42), 11694.

(11)

Bradski, G. The OpenCV Library. Dr. Dobb's Journal of Software Tools 2000.

(12)

Chmiel, M.; Slowinski, M.; Dasiewicz, K.; Florowski, T. Use of computer vision system (CVS) for detection of PSE pork meat obtained from m. semimembranosus. Lwt - Food Science and Technology 2016, 65, 532.

(13)

Capitán-Vallvey, L. F.; López-Ruiz, N.; Martínez-Olmos, A.; Erenas, M. M.; Palma, A. J. Recent developments in computer vision-based analytical chemistry: A tutorial review. Analytica Chimica Acta 2015, 899, 23.

(14)

DeCost, B. L.; Jain, H.; Rollett, A. D.; Holm, E. A. Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks. JOM 2017, 69 (3), 456.

(15)

Cantrell, K.; Erenas, M. M.; de Orbe-Payá, I.; Capitán-Vallvey, L. F. Use of the Hue Parameter of the Hue, Saturation, Value Color Space As a Quantitative Analytical Parameter for Bitonal Optical Sensors. Analytical Chemistry 2010, 82 (2), 531.

(16)

Protesescu, L.; Yakunin, S.; Bodnarchuk, M. I.; Krieg, F.; Caputo, R.; Hendon, C. H.; Yang, R. X.; Walsh, A.; Kovalenko, M. V. Nanocrystals of Cesium Lead Halide Perovskites (CsPbX3, X = Cl, Br, and I): Novel Optoelectronic Materials



Showing Bright Emission with Wide Color Gamut. Nano Letters 2015, 15 (6), 3692. (17)

Lignos, I.; Stavrakis, S.; Nedelcu, G.; Protesescu, L.; deMello, A. J.; Kovalenko, M. V. Synthesis of Cesium Lead Halide Perovskite Nanocrystals in a DropletBased Microfluidic Platform: Fast Parametric Space Mapping. Nano Lett 2016, 16 (3), 1869.

(18)

Smith, T.; Guild, J. The C.I.E. colorimetric standards and their use. Transactions of the Optical Society 1931, 33 (3), 73.

(19)

Sutton Rebecca, J.; Eperon Giles, E.; Miranda, L.; Parrott Elizabeth, S.; Kamino Brett, A.; Patel Jay, B.; Hörantner Maximilian, T.; Johnston Michael, B.; Haghighirad Amir, A.; Moore David, T.et al. Bandgap-Tunable Cesium Lead Halide Perovskites with High Thermal Stability for Efficient Solar Cells. Advanced Energy Materials 2016, 6 (8), 1502458.

(20)

Luo, Y. R. Comprehensive Handbook of Chemical Bond Energies; CRC Press, 2007.

(21)

Brus, L. E. Electron–electron and electron‐hole interactions in small semiconductor crystallites: The size dependence of the lowest excited electronic state. The Journal of Chemical Physics 1984, 80 (9), 4403.

(22)

Clark, M. D.; Kumar, S. K.; Owen, J. S.; Chan, E. M. Focusing Nanocrystal Size Distributions via Production Control. Nano Letters 2011, 11 (5), 1976.

(23)

Peng, X.; Wickham, J.; Alivisatos, A. P. Kinetics of II-VI and III-V Colloidal Semiconductor Nanocrystal Growth: “Focusing” of Size Distributions. Journal of the American Chemical Society 1998, 120 (21), 5343.

(24)

Lifshitz, I. M.; Slyozov, V. V. The kinetics of precipitation from supersaturated solid solutions. Journal of Physics and Chemistry of Solids 1961, 19 (1), 35.


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(25)

Wagner, C. Theorie der Alterung von Niederschlägen durch Umlösen (OstwaldReifung). Zeitschrift für Elektrochemie, Berichte der Bunsengesellschaft für physikalische Chemie 1961, 65 (7‐8), 581.

(26)

Allen, P. M.; Walker, B. J.; Bawendi, M. G. Mechanistic Insights into the Formation of InP Quantum Dots. Angewandte Chemie (International ed. in English) 2010, 49 (4), 760.

(27)

Owen, J. S.; Chan, E. M.; Liu, H.; Alivisatos, A. P. Precursor Conversion Kinetics and the Nucleation of Cadmium Selenide Nanocrystals. Journal of the American Chemical Society 2010, 132 (51), 18206.

Supporting information 1. Component design and control 1.1 Robot arm 1.2 Reaction chamber 1.3 Control and communication system 1.4 Schematics of the working procedure of the system 2. Implementation details 2.1 Object recognition and localization 2.2 Mapping and navigation 2.3 Conversion from RGB color bar to energy bar and wavelength pattern 2.4 Automatic synthesis and parameter self-adjusting experiment of CsPbX3 QDs 2.5 In-situ monitoring of CsPbBr3 QDs size growth based on CV technology 3. Details of the multi-injection theory model



Movie 1. Automatic synthesis and real-time characterization of CsPbBr3 QDs Movie 2. Pigment-mix experiment with parameter self-adjusting


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AIR-Chem: an Authentic Intelligent Robotics for Chemistry - The

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