Chemical Science Blog

Dye-sensitized solar cells (DSSCs) are electrochemical devices that can convert solar energy into electricity. The critical component of a DSSC is its dye molecules which are covalently adsorbed on the electrodes of the DSSC. These molecules are responsible for light absorption and energy conversion in the device. DSSCs are more economical than commercial Si-based solar cells, but their lifetimes are limited (~6 years vs. 20-30 years of Si-based counterparts).

A research team from Xiamen University, China, recently demonstrated in Chemical Science (DOI: 10.1039/D0SC00588F) that the anchoring stability of the dyes determined the longevity of DSSCs. They specifically studied N719, a Ru-containing dye, adsorbed on three different crystal facets of rutile TiO₂ (electrode). N719 adsorbed on the TiO₂(111) facets was the most stable among all the facets studied.

The researchers adopted surface-enhanced Raman spectroscopy (Fig.1) in their research, the setup of which involved two laser beams. One 405-nm laser excited the dye molecules to initiate energy conversion, and another 638-nm laser collected the Raman scattering signals at the dye/TiO₂ interface. The obtained Raman spectra showed the peaks associated with the vibrations of N719.

Figure 1. The experimental setup. The 405-nm laser excites N719 dye molecules adsorbed on rutile TiO2. The 638-nm laser probed the Raman scattering signals of N719. The Au nanoparticle (yellow sphere) enhances the Raman signal intensity.

The Raman spectra revealed that the adsorption stability of N719 depended on the crystal facet of TiO₂. For TiO₂(001), after illumination for 36 min, the Raman peaks of N719 gradually diminished (Fig. 2a), indicating that the dye molecules were either detached from TiO₂ or decomposed. A similar trend was observed for N719 on TiO₂(110) (Fig. 2b). Mass spectroscopy detected that the electrolytes after 36-min illumination contained N719 molecules missing an S atom. This result indicated that the C=S bond of N719 was broken, leading to the loss of the dye. In contrast, N719 on TiO₂(111) exhibited stable Raman signals during the identical illumination duration (Fig. 2c).

The different stability was ascribed to variation in the dissociation energy of the C=S bond. Density functional theory (DFT) simulation proved that the cleavage of the C=S bond on TiO₂(111) had an energy barrier of 3.5 eV, about 1.0 eV and 1.5 eV higher than those on TiO₂(110) and TiO₂(001), respectively. The higher energy barrier suppresses bond dissociation and stabilizes the adsorption of N719.

Figure 2. Raman spectra of N719 adsorbed on (a) TiO2(001), (b) TiO2(110), and (c) TiO2(111). Spectra were collected with an interval of 4 min. Peaks highlighted in yellow and blue are from TiO2 and N719, respectively. The schemes on the right show the simulated structures of dye-adsorbed (top) and desorbed (bottom) TiO2 facets. Ph–N=C=S represents N719 in simulation. The yellow spheres are S.

This work highlights the importance of dye positioning in promoting long-lasting performance of DSSCs.

For expanded understanding, please read:

In Situ Raman Study of the PhotoInduced Behavior of Dye Molecules on TiO₂(hkl) Single Crystal Surfaces

Sheng-Pei Zhang, Jia-Sheng Lin, Rong-Kun Lin, Petar M. Radjenovic, Wei-Min Yang, Juan Xu, Jin-Chao Dong, Zhi-Lin Yang, Wei Hang, Zhong-Qun Tian, and Jian-Feng Li

Chem. Sci., 2020, DOI: 10.1039/D0SC00588F

Tianyu Liu acknowledges Zacary Croft at Virginia Tech, U.S., for his careful proofreading of this post.

About the blogger:

Tianyu Liu obtained his Ph.D. (2017) in Chemistry from the University of California, Santa Cruz, in the United States. He is passionate about the communication of scientific endeavors to both the general public and other scientists with diverse research expertise to introduce cutting-edge research to broad audiences. He is a blog writer for Chem. Comm. and Chem. Sci. More information about him can be found at http://liutianyuresearch.weebly.com/.

It should go without saying that NMR is an incredibly important characterization technique with profoundly broad applicability across the entirety of chemistry. Rarely do you find something that people who work on proteins and wacky main-group synthesis both consider crucial to their work. Given powerful enough magnets and high-quality samples, rich structural information can be obtained for all manner of molecules large and small. Large molecules do pose a problem with the sheer volume of information contained within a single spectrum. Because of this, there exists a need to develop computational programs that can translate spectra into detailed structural models. Currently, existing methods predict NMR spectra based on a combination of experimentally based databases with chemical shift heuristics. These simulations, while useful, lack high predictive rigor and often have difficulty simulating the messiness of real world data. This is particularly challenging because experimental spectra can often have significant chemical shift deviations from predicted values, with those peaks discarded as outliers.

Figure 1. The overall design of the novel UCBShift chemical shift prediction algorithm, combining both a transfer prediction module a machine learning module.

To face these challenges and generate more accurate results, researchers in the US developed a new algorithm that uses both machine learning and transfer prediction (Figure 1). Transfer prediction has been widely used and relies on the similarities of NRM peak sequences between known data, typically clean datasets, and the experimental sample in question. The advantage of the new approach is that it allows for data that would previously have been dismissed as anomalous to be utilized and to give more accurate predictions. The researchers used high-quality datasets that they modified for accuracy. In particular, they retained the water and ligand molecules that co-crystallized with the proteins that would likely be associated with the solvated forms of the proteins. As the interactions of these small molecules can alter the spectral shifts of NMR peaks, their inclusion increases the likelihood that peaks previously considered outliers will be incorporated and analyzed.

Figure 2. Difference between UCBShift-Y and SHIFTY+ (previous method) showing that overall the new algorithm is making better predictions.

Initial analysis with the new dataset produced some anomalous results, which were then mitigated by removing paramagnetic and other outlier proteins that would bias the results against the earlier algorithms. Once those were removed, the new algorithm still outperformed prior methods (Figure 2). While these advances are extremely useful for current researchers, they are approaching the limit of accuracy for systems that rely heavily on transfer predictions. In order to generate fully accurate models and structures intense work on combining deep learning with human expertise is necessary.

To find out more, please read:

Accurate prediction of chemical shifts for aqueous protein structure on “Real World” data

Jie Li, Kochise C. Bennett, Yuchen Liu, Michael V. Martin and Teresa Head-Gordon

Chem. Sci., 2020,11, 3180-3191

About the blogger:

Dr. Beth Mundy is a recent PhD in chemistry from the Cossairt lab at the University of Washington in Seattle, Washington. Her research focused on developing new and better ways to synthesize nanomaterials for energy applications. She is often spotted knitting in seminars or with her nose in a good book. You can find her on Twitter at @BethMundySci.

We are pleased to share a selection of our referee-recommended HOT articles for April. We hope you enjoy reading these articles and congratulations to all the authors whose articles are featured! As always, Chemical Science is free to read & download. You can find our full 2020 HOT article collection here.

What is the role of acid–acid interactions in asymmetric phosphoric acid organocatalysis? A detailed mechanistic study using interlocked and non-interlocked catalysts
Dennis Jansen, Johannes Gramüller, Felix Niemeyer, Torsten Schaller, Matthias C. Letzel, Stefan Grimme, Hui Zhu, Ruth M. Gschwind and Jochen Niemeyer
Chem. Sci., 2020, Advance Article
DOI: 10.1039/D0SC01026J

Structural properties of ultra-small thorium and uranium dioxide nanoparticles embedded in a covalent organic framework
Liane M. Moreau, Alexandre Herve, Mark D. Straub, Dominic R. Russo, Rebecca J. Abergel, Selim Alayoglu, John Arnold, Augustin Braun, Gauthier J. P. Deblonde, Yangdongling Liu, Trevor D. Lohrey, Daniel T. Olive, Yusen Qiao, Julian A. Rees, David K. Shuh, Simon J. Teat, Corwin H. Booth and Stefan G. Minasian
Chem. Sci., 2020, Advance Article
DOI: 10.1039/C9SC06117G

Iron porphyrin catalysed light driven C–H bond amination and alkene aziridination with organic azides
Yi-Dan Du, Cong-Ying Zhou, Wai-Pong To, Hai-Xu Wang and Chi-Ming Che
Chem. Sci., 2020, Advance Article
DOI: 10.1039/D0SC00784F

The assemble, grow and lift-off (AGLO) strategy to construct complex gold nanostructures with pre-designed morphologies
Xin Luo, Christophe Lachance-Brais, Amy Bantle and Hanadi F. Sleiman
Chem. Sci., 2020, Advance Article
DOI: 10.1039/D0SC00553C

Geometric Landscapes for Material Discovery within Energy-Structure-Function Maps
Seyed Mohamad Moosavi, Henglu Xu, Linjiang Chen, Andrew Cooper and Berend Smit
Chem. Sci., 2020, Accepted Manuscript
DOI: 10.1039/D0SC00049C

Expedient synthesis of conjugated triynes via alkyne metathesis
Idriss Curbet, Sophie Colombel-Rouen, Romane Manguin, Anthony Clermont, Alexandre Quelhas, Daniel S. Müller, Thierry Roisnel, Olivier Baslé, Yann Trolez and Marc Mauduit
Chem. Sci., 2020, Advance Article
DOI: 10.1039/D0SC01124J

Transferring Axial Molecular Chirality Through a Sequence of On-Surface Reactions
Néstor Merino-Díez, Mohammed S. G. Mohammed, Jesus Castro, Luciano Colazzo, Alejandro Berdonces-Layunta, James Lawrence, Jose Ignacio Pascual, Dimas G. de Oteyza and Diego Peña
Chem. Sci., 2020, Accepted Manuscript
DOI: 10.1039/D0SC01653E

Submit to Chemical Science today! Check out our author guidelines for information on our article types or find out more about the advantages of publishing in a Royal Society of Chemistry journal.

Keep up to date with our latest articles, reviews, collections & more by following us on Twitter. You can also keep informed by signing up to our E-Alerts.

If you are a polymer chemist, you may have dreamt of having a pair of eyes that can directly tell whether your polymerization reaction is working or not. Good news! Randall H. Goldsmith of the University of Wisconsin-Madison, U.S. and coworkers have developed an optical technique to monitor the course of a polymerization reaction in real-time. This breakthrough has been published in Chemical Science (DOI: 10.1039/C9SC05559B).

This technique relies on two parameters, fluorescence polarization anisotropy and aggregation-induced emission, to examine molecular weights. The authors specifically studied a well-controlled polymerization of norbornene (NB) into polynorbornene. Besides monomer, they added small amounts of two fluorescent probes, perylene diimide-functionalized norbornene (PDI-NB) and tetraphenylethylene-linked norbornene (TPE-NB), to the polymerization system. These NB derivatives were co-polymerized with NB (Fig.1).

Figure 1. Polymerization of norbornene (NB) catalyzed by a ruthenium-based Grubbs Generation II catalyst (Grubbs Gen II). PDI-NB and TPE-NB are two probes co-polymerized with NB to monitor the growth of polynorbornene.

The technique temporally resolved the evolution of polynorbornene molecular weight. The polymerization incorporated the probe molecules into the backbones of polynorbornene. As polymer chains grew, the rotational freedom of PDI-NB became increasingly limited, giving rise to an enhanced anisotropy signal during the first 200 min of polymerization (Fig. 2a, top panel and Fig. 2b, red curve). Further polymerization brought the incorporated TPE-NB together, triggering the aggregation-induced fluorescence emission of TPE-NB (Fig. 2a, bottom panel and Fig. 2b, blue curve). Importantly, the rise of the emission intensity from TPE-NB immediately followed the saturation of anisotropy signal from PDI-NB, making the two molecules complementary for monitoring the polymer growth in different time scales. Critically, the anisotropy signal intensity of PDI-NB correlated positively with the weight-average molecular weight of polynorbornene (Fig. 2c), demonstrating the capability of this technique to track the progression of polymer growth.

Figure 2. (a) (top) Color-scale images of anisotropy values of PDI-NB and (bottom) emission intensities of TPE-NB over time. Dots are top-view toluene microdroplets where polymerization happens. Scale bar: 250 µm. (b) Time-evolution of average anisotropy values of PDI-NB (red) and aggregation-induced-emission (AIE) intensity of TPE-NB (blue) throughout a polymerization. (c) The correlation between measured anisotropy values and the weight-average molecular weights of polynorbornene. The red dashed line provides a visualization of the trend.

The reported technique is applicable to other ring-opening metathesis polymerizations (ROMPs) involving monomers such as norbornadiene.

For expanded understanding, please read:

Optical Monitoring of Polymerizations in Droplets with High Temporal Dynamic Range

Andrew C. Cavell, Veronica K. Krasecki, Guoping Li, Abhishek Sharma, Hao Sun, Matthew P. Thompson, Christopher J. Forman, Si Yue Guo, Riley J. Hickman, Katherine A. Parrish, Alán Aspuru-Guzik, Leroy Cronin, Nathan C. Gianneschi, and Randall H. Goldsmith

Chem. Sci., 2020, 11, 2647-2656.

The blogger acknowledges Zac Croft at Virginia Tech, U.S., for his careful proofreading of this post.

About the blogger:

Tianyu Liu obtained his Ph.D. (2017) in Chemistry from the University of California, Santa Cruz, in the United States. He is passionate about the communication of scientific endeavors to both the general public and other scientists with diverse research expertise to introduce cutting-edge research to broad audiences. He is a blog writer for Chem. Comm. and Chem. Sci. More information about him can be found at http://liutianyuresearch.weebly.com/.