Using Nanopore Sensors to Analyze and Characterize Heparin and Other Therapeutic Polysaccharides 

Studied by Terahertz (THz) Spectroscopy and Density Functional Theory (DFT)

The Hydrogen Bonding Mode of 5-Fluorouracil-Proline Co-Crystal (5F-P) 

An Exploration of Chemometrics and Mathematics

Why Does -1 x -1 = 1?

Quantitative Methods for Multielemental Analysis in Low-Volume Biofluids

Michael J. Hennessy Jr. is a man on a mission. The president and CEO of MJH Life Sciences™ was recently included in MM&M’s second annual 40 Under 40 list. The award celebrates inspiring young talent in various areas of pharmaceutical and healthcare marketing who are poised to lead the industry.

As a third-generation member of the family-run MJH Life Sciences, healthcare has always been part of Hennessy Jr.’s life, but he hasn’t used his position to coast on the tailwinds provided to him. Instead, he has fundamentally reinvented the largest privately held, independent, and full-service medical media company in North America by innovating and investing in key growth areas.

Hennessy Jr. received his BBA in marketing from Loyola University Maryland and began his career as a national account manager at MJH Associates in 2010. From there, he took on several sales leadership roles, including the day-to-day management of OncLive®, Specialty Pharmacy Times®, and the Healthcare Specialty Group. In September 2015, he was elevated to president of the company. As president, Hennessy Jr. was responsible for identifying new business opportunities and driving the overall growth of the organization. Shortly after MJH Associates was rebranded as MJH Life Sciences in 2019, he was promoted to president and chief executive officer.

Since the beginning of his career, Hennessy Jr. has demonstrated his vision for creating new and engaging ways to serve healthcare professionals. As senior vice president of the Oncology Specialty Group, he relaunched OncLive.com, propelling the brand to new heights with a digital and video-first approach. He led the launch of a range of video offerings, such as the 

 video programs, that provided the Oncology audience more engaging content options. He then applied this successful model to other brands in the portfolio.

As president, Hennessy Jr. took the initiative to innovate the company’s events offerings, focusing on case-based learning, roundtables, regional seminar series, and award programs. These events built large audiences and gained credibility with industry leaders, further enhancing the MJH content creation model.

After overseeing sustained, organic growth, Hennessy Jr. further strengthened MJH by acquiring UBM Life Sciences Group in January 2019. The acquisition included the addition of seven new offices and more than 220 associates. Hennessy Jr. led the integration of the UBM assets, streamlining the company and focusing on reinvigorating the new brands to deliver on MJH’s vision of helping healthcare professionals improve patient care.

Most recently, Hennessy Jr. employed his visionary leadership to guide MJH through the COVID-19 pandemic. He leveraged the company’s state-of-the-art studios to quickly pivot live events to virtual ones without missing a beat. As a result, the company produced a record number of events that included many new, innovative offerings. In June, MJH launched 

, a first-of-its-kind news channel for healthcare professionals. He then introduced the MJH Life Sciences 

, a partnership with healthcare thought leaders devoted to providing ongoing insight around the pandemic and patient care. MJH has steadily grown under Hennessy Jr.’s leadership to 60 brands, producing 975 events, and reaching 7.3 million unique visitors per month online and 2 million via print.

Most of Hennessy Jr.’s investment has been focused on growth areas in the healthcare industry. He’s created a platform to deliver unparalleled content to audiences in oncology, infectious disease, managed care, and pharmaceutical manufacturing, to name a few, and he continues to add new content where he envisions further growth. For example, the company is launching 

Hennessy Jr. has been agile in focusing on the modern ways audiences consume content, especially since the pandemic began. While continuing to publish MJH category-leading print magazines, his leadership has embraced a digital-first approach focused on well-designed, user-centric websites; video content and video series; podcasts; on-demand content and education; and even digital broadcast. He also led the creation of a state-of-the-art in-house digital studio.

The company’s use of leading-edge marketing approaches has also been a priority. Hennessy Jr. led the implementation of enterprise data and technology platforms that enable MJH’s businesses to drive even greater user-centric media practices. He’s embraced and enabled data-driven programming for MJH and its partners­­—from dynamic web and digital content delivery to automated, omnichannel journey creation to dashboards and analytics.

Beyond MJH, Hennessy Jr. has contributed to the industry at large by fostering and creating personal and content-based relationships with world-renowned key opinion leaders in the medical field and at leading universities, medical centers, hospitals, medical associations, and community cancer centers through MJH’s Strategic Alliance Partnership (SAP) program. The program provides its partners and doctors with a national reach and visibility, utilizing the breadth of MJH’s medical communications platform to showcase cutting-edge initiatives, content, research, and thought leadership that ultimately benefit patients and their families.

This past year, Hennessy Jr. capitalized on an opportunity to grow the business by elevating the SAP program by collaborating with health insurance companies such as Highmark Health and Oscar Health to expand its footprint in the managed care marketplace. Additionally, when forming the MJH Life Sciences COVID-19 Coalition advisory board, he leveraged his existing relationships with several top thought leaders across a variety of key specialties, including Dr. Angela Rasmussen, Dr. Saskia Popescu, and Dr. Paul E. Sax, to help generate the most accurate, up-to-the-minute information on the pandemic’s ever-evolving impact on healthcare professionals and the patients they treat.

Throughout the pandemic, Hennessy Jr. has donated and supported several organizations, including the American Heart Association, NJ Pandemic Relief Fund, Princeton Medical Healthcare Heroes, Make-A-Wish NJ, National Medical Fellowships, Susan G. Komen Foundation, Feeding America, among many others. He also has volunteered his time at the RISE: Greater Goods Thrift Store in Hightstown, New Jersey; HomeFront in Ewing, New Jersey; and the Trenton Area Soup Kitchen (TASK).

Working alongside his father, brother, and cousins in the organization certainly qualifies Hennessy Jr. as a family man. Outside of the office, he loves spending quality time with his wife, Rachel, and two children, MJ and Maggie. He is a fitness aficionado who enjoys running and CrossFit. He also enjoys watching sports—specifically New York Yankees baseball. Relaxing to county music and enjoying the beauty of the Jersey Shore with friends and family is also time he cherishes.

Hennessy Jr. may have been born into the MJH Life Sciences business, but he’s reinvented it in his own image. And while he’s positioned the company for longstanding future growth, don’t expect him to rest on his laurels. He’s no doubt already thinking of ways to innovate the healthcare media landscape even further.

Articles

Michael J. Hennessy Jr., president and CEO of MJH Life Sciences™, parent company to Spectroscopy, was named to MM&M’s second annual 40 Under 40 list.

MJH Life Sciences President and CEO Michael J. Hennessy Jr. Recognized as MM&M 40 Under 40 Honoree

In March 2020, the United States was in the early stages of the COVID-19 pandemic. We shut the entire country down and ground the economy to a halt in an effort to slow the spread of the virus. Think back to March, and how much uncertainty we were living under.

Nine months later, the FDA approved two COVID-19 vaccines under emergency authorization. Before New Year’s Day, millions of Americans had received the vaccine, including front-line physicians and health care providers and nursing home patients, our most vulnerable citizens.

Nine months. Take a moment to let that sink in.

The mainstream media has crafted a narrative around the COVID-19 pandemic that’s almost entirely negative. For the purpose of ratings, they have described the U.S. response to the pandemic as blundering from one mistake to the next. This narrative is false.

There is another way — a more accurate and underappreciated way — to tell the story of the last nine months. It is a story of heroism, innovation, and precise science, performed under unbelievable pressure.

Let’s not mince words: The United States and the world needs to appreciate the role of the pharmaceutical industry — the researchers, physicians and business leaders — who are rescuing the world from COVID-19. It’s the medical breakthrough of our lifetime.

Instead of dwelling on why many in the media are ignoring this, let’s review some facts.

Since the discovery of COVID-19, here is what scientists have accomplished: They identified a novel virus, unlocked and sequenced its genetic code, created new therapies to save lives, and developed multiple safe and effective vaccines using messenger RNA technology, a technology hopefully applicable to future vaccine development. Margaret Liu, MD, a member of the MJH Life Sciences COVID Coalition, called it a breakthrough for mRNA vaccines.

The United States has two vaccines approved for emergency use, one from Pfizer/BioNTech and another from Moderna, and the AstraZeneca/Oxford vaccine has been approved for emergency use in the UK. In addition, there are 64 vaccines undergoing clinical trials at the moment, including 20 in phase 3 trials. In the United States and around the world, the pharmaceutical industry has answered the call and invested heavily in this effort.

This was the fastest vaccine development program in history, and it’s not even close. David Pride, MD, a microbiologist at the University of California San Diego, estimates that vaccines typically take 10 to 15 years to develop. Until the COVID-19 pandemic, the fastest development timeline was four years, for the mumps vaccine.

Many government systems moved quickly to lessen the burden of onerous regulations and provide funding so that vaccines could be developed quickly but with still rigorous standards. Perhaps it should be a lesson to all of us that regulation/innovation can be calibrated more effectively during “normal” times as industry races to develop new therapies for our world’s other pandemics — cancer, diabetes, heart diseases, and more.

The next step of the process — distribution of the vaccine — will be as challenging as the development phase, if not more so. But again, the pharmaceutical industry is rising to the occasion. Factories around the world are working in overdrive to produce hundreds of millions of vaccine doses.

Already, less than a month after the Pfizer vaccine was approved, more than 15.4 million doses of vaccine have been distributed across the country, and more than 4.6 million people have received their first dose, according to CDC data. Many patients are already receiving their second dose.

While 15.4 million doses are impressive, some expected 20 million doses. But even that is moving the goal line a bit, as six months ago many observers didn’t think we’d get a vaccine until 2021.

Members of our COVID Coalition told us that the holidays slowed the rollout considerably. Nancy Messonnier, MD, a physician with the National Center for Immunization and Respiratory Diseases at the CDC, expects a rapid jump in administered vaccines during these first few days of 2021.

Every day, more people will be vaccinated. After health care workers and our most vulnerable citizens, other frontline workers will be next. Teachers will be vaccinated so our children can return to school. And soon, all Americans will be able to go to their doctor or walk into a CVS or Walgreens and receive the vaccine.

Remember, we did all this in nine months, with the help, dedication and expertise of our pharmaceutical industry heroes. Next time you turn on the TV and see negativity, turn it off and imagine instead where we will be nine months from now.

Mike Hennessy Sr is the founder and chairman of MJH Life Sciences.

The COVID-19 vaccine, and the speed at which it was developed, is the medical breakthrough of our lifetimes.

How Pharmaceutical Innovation is Saving the World

The hydrogen bonding mode of 5-fluorouracil-proline co-crystal (5F-P) was investigated using terahertz time-domain spectroscopy (THz-TDS) and density functional theory (DFT). The THz spectrum of 5F-P is significantly different from the monomer of 5-fluorouracil (5- FU) and proline within the range of 0.25–2.6 THz. This result indicates that the two monomers react to form a new phase. Based on unit cell simulation, the theoretical spectrum of configuration 2 (N1–H1•••O4) is the most consistent with the experimental spectrum. Furthermore, configuration 2 carries the lowest amount of total energy and exhibits the most stable structure. Therefore, the hydrogen bonding mode of configuration 2 is closest to 5F-P. This work shows that the combination of THz-TDS and DFT can provide an effective method to investigate the hydrogen bonding mode of the new phase.

L-proline (L-Pro) has a unique structure among the 20 amino acids normally found in proteins because it is the only imino acid among them. The unique cyclic structure leads to the conformational rigidity and specific imino functionality compared to amino acids (1,2). The amino nitrogen binds with the side chain, with the α-carbon leading to a pyrrolidine ring. This structure restricts the conformations that proline can adopt within a peptide or protein, giving it a unique role in the secondary and tertiary structures of proline-containing proteins (3,4). Given this conformation, proline is often found in complex proteins. It plays a binding role in collagen, which has a high proline content. Proline-rich regions in proteins are also found in many kinase binding sites and are linked to many other cellular processes (5–8). Proline is an amphoteric substance with both carboxyl and amino groups. Its solubility in aqueous solutions is more soluble than other amino acids. Proline has also been shown to have strong water absorption in inelastic incoherent neutron scattering studies (9). Proline contains an imino (-NH-) group and a carboxyl (-COOH) group, which can form both intramolecular hydrogen bonds and intermolecular hydrogen bonds with other molecules (10).

In 1957, Heidelberger and others (11) first discovered 5-fluorouracil (5-FU). They replaced the hydrogen atom at the fifth position of uracil with a fluorine atom of similar size. The obtained fluoride is not only similar in volume to the original compound but also the carbon–fluorine bond formed is very stable, which is not easy to decompose during metabolism and can interfere with normal metabolism at the molecular level. 5-FU is an antimetabolic tumor drug, and its active metabolite inhibits thymidine synthase (TS), which is the primary mechanism for its antitumor effect (12). However, its actual efficacy is affected to a certain extent because of the low solubility of 5-FU. Because proline is nontoxic and soluble in water, it is an ideal co-crystal former (CCF). It can effectively improve the solubility of 5-FU by co-crystallization of 5-FU and proline (5F-P). However, its toxicity and anticancer need further research in medicine. The molecular structure of proline and 5-FU is shown in Figure 1.

FIGURE 1: Molecular structure of (a) proline and (b) 5-FU.

The drug co-crystal refers to a crystal formed by combining active pharmaceutical ingredient (API) and CCF in a particular proportion under the action of hydrogen bonds or other non-covalent bonds (13–15). 5-FU, as an active pharmaceutical ingredient, combines with proline to form a co-crystal through hydrogen bonding. The hydrogen bond is usually expressed as A—H•••B. Although hydrogen bonding is a weak interaction, it plays a vital role in affecting biomolecules’ physical and chemical properties. In recent years, researchers have done a lot of research on noncovalent interactions among molecules, including experimental methods such as infrared, Raman, and nuclear magnetic resonance spectroscopy, as well as calculation methods such as molecular dynamics simulation (16–18).

As a novel technique, terahertz (THz) spectroscopy is regarded as an advanced approach that studies new aspects of molecular structure and distinguishes polymorphic pharmaceutical, which is especially useful when studying biomolecules, which contain rich hydrogen-bond interactions (19–22). THz spectroscopy could directly collect helpful information on low-frequency vibrations shown within specific molecular structures and intermolecular hydrogen bonding interactions. Meanwhile, computational methods, such as solid-state density functional theory (DFT), have shown potential as reliable approaches to studying these low-energy lattice vibrational motions (23–26). Thus, the combination of DFT and experimental data is a very effective method to study the molecular geometry and intermolecular interactions. Applications of terahertz time-domain spectroscopy (THz-TDS) on weak interaction and co-crystal have been reported in previous studies. Sean P. Delaney and others demonstrated that the co-crystal of flufenamic acid and nicotinamide has better overall binding energy by THz-TDS and solid density (27). Chihiro Funaki and others proved that the existence of three different kinds of weak C-H•••O=C interactions in poly (ε-caprolactone) by THz-TDS (28). Qi Zhou and others characterized the co-crystal of carbamazepine with nicotinamide, saccharin, and fumaric acid by THz-TDS and DFT (29).

In this study, we successfully obtained the co-crystal of proline and 5-FU by evaporating the solvent. As a new phase, 5F-P co-crystal has no detailed report on its hydrogen bonding mode, which is significant for studying the properties of substances. Therefore, we investigated the hydrogen bonding mode of 5F-P by using THz-TDS and DFT. Furthermore, we also verified the accuracy of the hydrogen bonding model of 5F-P through the stability of the system.

L-Pro (purity > 98%), 5-FU (purity > 98%) and polyethylene (PE, particles size 40–48 μm) were purchased from Sigma-Aldrich. All chemicals were used without further purification.

Proline and 5-FU were placed in a 100 mL beaker at a 1:1 molar ratio (690 mg:780 mg) and added 70 mL of a methanol:ethyl and an acetate:acetone mixed solvent in a volume ratio of 1:1:1 to the mixture. It was then heated and stirred in a constant temperature water bath for about 2 h at 60 °C. Finally, the obtained solution was filtered under reduced pressure to remove insoluble matters to get clear solution. It was slowly evaporated at normal temperature for about three days to collect the solid phase. The obtained solid phase was dried overnight to obtain 5F-P in a constant temperature environment at 25 °C.

Samples and PE were weighed into agate mortar at 1:5 (weight of sample/weight of PE = 40 mg/200 mg) and ground gently into fine particles with a pestle to thoroughly mix samples with PE. The samples were then compressed into a pellet (about 1.9 mm in thickness and 13 mm in diameter) with a hydraulic press using 10 tons pressure for 3 min.

The THz-TDS used in the experiment was made by Daheng Optoelectronics. The center wavelength of the CIP-TDS spectrometer was 800 nm, and the repetition frequency was 84 MHz. The effective spectral band was 0.2–3 THz. The samples were placed into the THz system, purged with high-purity nitrogen and kept at the relative humidity <1%. The THz spectrum of each sample was measured at room temperature (20 °C), and PE was taken as a reference. Each spectrum was an average of three measurements. One measurement included 1024 scan numbers. The time-domain of the THz electric field was recorded for the reference and each sample. The fast Fourier-transform (FFT) operation obtained the time-domain spectrum, and the THz spectral absorption was obtained by dividing the sample frequency response by that of the reference.

The PXRD was purchased from Thermo ARL, Switzerland, for this experiment. The X-ray source was a copper shoe, and the working voltage was 40 kV. The working current was 40 mA. The scanning range was 5 to 50°, and the scanning speed was 0.2°/step.

The theoretical calculations in this study were all performed using the Cambridge Sequential Total Energy Package (CASTEP) (30). There was no virtual frequency in all simulation calculations, which indicates that the optimized structure achieved a stable structure corresponding to the lowest molecular energy. The exchange- related functional used generalized gradient approximation–Perdew-Burke-Ernzerhof (GGA–PBE) (31). The quality of the energy calculation was ultra-fine. The quality of the plane wave cut off energy was ultra-fine, and the energy cutoff was 830 eV. Norm conserving was selected as the pseudopotential. The quality of the 

-point set was fine. The convergence thresholds for energy, maximum force, stress, and maximum displacements was 5.0 × 10

 (eV/atom), 0.01 eV/Å, 0.02 GPa and 5.0 × 10

 Å, respectively. The Grimme correction was also added to the calculation task to evaluate weak interactions, such as hydrogen bonds among molecules (32).

Powder X-ray Diffraction (PXRD) of Proline, 5-FU and 5F-P 

Figures 2a, 2b, and 2c shows the PXRD patterns of proline, 5-FU and 5F-P. Figure 2d is the simulated PXRD based on the unit cell of 5F-P. The diffraction peaks of proline are located at 15.28°, 18.2°, 19.72°, 24.94°, 30.76°, 32.34°, and 39.96°, respectively. The diffraction peaks of 5-FU are located at 11.22°, 15.7°, 16.12°, 18.83°, 21.69°, 28.43°, 32.69°, and 36.95°. The results are consistent with previous reports (33,34). The PXRD of 5F-P is significantly different from the two monomers. The comparison of PXRD with the Materials Data Inc. (MDI) Jade 6.5 PDF standard card showed that the proline is orthorhombic with space group P212121, a = 11.626 Å, b = 9.024 Å, c = 5.246 Å, α = β = γ = 90° (PDF #21-1805), but the 5-FU is triclinic with space group P-1, a = 9.22Å, b=12.66Å, c=12.67Å, α=89.7°, β = 43.9°, γ = 98.6° (PDF #39-1860). Since 5F-P is a new phase, the crystal structure does not appear in the Cambridge Crystallographic Data Center (CCDC). We used the Reflex module to index the co-crystal based on the PXRD diffraction peak. The result after further refinement is that the 5F-P is triclinic, the space group is P-1 with a = 11.13 Å, b = 8.12 Å, c = 6.21 Å, α = 88.65°, β = 104.89°, γ = 107.43°. As shown in Figure 2d, the simulation PXRD pattern based on indexed data is consistent with the experimental pattern of 5F-P. Therefore, these results show that the simulated unit cell of 5F-P is reasonable.

FIGURE 2: Powder X-ray diffraction (PXRD) pattern of (a) proline, (b) 5-FU, (c) 5F-P and (d) the simulated PXRD pattern of 5F-P.

THz Absorption Spectra of Proline, 5-FU, and 5F-P

The THz experimental spectrum of monomers, co-crystal and mixture (mixed at 1: 1 molar ratio) are shown in Figure 3. The THz spectrum of proline exhibits a noticeable absorption peak at 1.99 THz, while 5-FU has a strong absorption at 2.46 THz, as shown in Figure 3a. There are few absorption peaks of proline and 5-FU. From Figure 3b, it can be seen that the THz absorption spectra of 5F-P display four visible absorption peaks, which are 0.98, 1.32, 1.55, and 1.99 THz, respectively. Furthermore, there is a weak absorption peak observed at 1.11 THz, which gives rise to a shoulder peak with the absorption peak at 0.98 THz. The THz spectrum of the mixture shows a weak absorption band at 1.99 THz. Besides, there is a suspicious absorption peak at 2.46 THz. It can be demonstrated that the absorption peak of the mixture is a simple superposition of two monomers. The THz absorption spectra of 5F-P is significantly different from the monomers and their mixture. Therefore, both the THz spectrum and PXRD show that the new solid phase is a co-crystal of the two reactants rather than a simple polymorphic transition of the single crystal. The crystal structure of the co-crystal has changed to some extent. Furthermore, a series of new absorption peaks appeared after the co-crystallization, which is attributed to the hydrogen bonding between the two molecules. Because the THz absorption peak of 5F-P is much more than two monomers, 5F-P produces more vibration modes. The formation of hydrogen bonds changes the arrangement of electrons, while the THz band is highly sensitive to hydrogen bonding interactions and collective vibrations of molecular lattices (35,36), thus resulting in corresponding changes in vibration intensity and frequency. The new absorption vibration peaks indicate that new hydrogen bonding interactions are produced in the co-crystal. Apart from that, the THz spectrum also can intuitively reflect the occurrence of intermolecular vibration.

FIGURE 3: The THz absorption spectra of (a) Pro, 5-FU. (b) The THz absorption spectra of co-crystal and mixture.

Natural Bond Orbital (NBO) Calculation of Charge Distribution

First of all, we have investigated potential intermolecular hydrogen bonding sites of 5F-P by the NBO calculation. NBO analysis is an effective tool for studying charge transfer, electronic transitions, and conjugate interactions. NBO atomic charge distribution was calculated at the B3LYP/6-311++G (d,p) level. Figure 4 shows the charge distribution of the stronger electronegative atoms in the proline (a) and 5-FU (b). There are two stronger polarity and negatively charged oxygen atoms O1 (-0.611e) and O2 (-0.694e) observed on the carboxyl group of proline. In addition, there is a stronger electronegative nitrogen atom N1 (-0.675e) spotted on the amino group. Also, there are strongly electronegative atoms on 5-FU, including two carbonyl oxygen atoms O1 (-0.564e), O2 (-0.612e) and two nitrogen atoms N1 (-0.650e), N2 (-0.605e). According to previous reports, both amino and carboxyl groups are more active groups. The highly electronegative atoms present on proline and 5-FU cause an uneven charge distribution. Therefore, there is the possibility of hydrogen bond formation between proline and 5-FU. In addition, proline is an effective co-crystal former. All these indicate that proline can easily form hydrogen bonds with 5-FU.

FIGURE 4: The atomic charge distribution of (a) Pro and (b) 5-FU. (The blue dashed line is hydrogen bonding). The various atoms are identified in the figure.

Table I shows the intermolecular hydrogen bond network of proline and 5-FU through previous reports (37,38). It can be seen that the imino and carboxyl groups on proline form intermolecular hydrogen bonds. The carbonyl oxygen atoms and imino groups on 5-FU participate in forming the intermolecular hydrogen bond network. This result is consistent with the NBO analysis in section 4.3. Therefore, the theoretical configuration is based on the formation of hydrogen bonds between strongly electronegative groups. In this study, 11 reasonable hydrogen bonding modes were obtained. The theoretical configuration of 5F-P was constructed based on these modes. We further optimized the unit cell of 11 theoretical configurations, two of which failed to achieve convergence. Therefore, we have obtained nine stable theoretical configurations of 5F-P and their hydrogen bond modes are shown in Figure 5. The hydrogen bonding modes of configurations 1–9 are N2–H2•••O4 (C1), N1–H1•••O4 (C2), N2–H2•••O3 (C3), N1–H1•••O3 (C4), N3–H12•••O2 (C5), N3–H12•••O1 (C6), N1–H1•••O3/ N3–H11•••O1 (C7), N1–H1•••O3/N3– H11•••O2 (C8), and N2–H2•••O3/N3– H11•••O2 (C9).

FIGURE 5: The hydrogen bonding mode of theoretical configuration (a) 1, (b) 2, (c) 3, (d) 4, (e) 5, (f) 6, (g) 7, (h) 8, (i) 9. The color code for atoms is given in the figure.

We have calculated the theoretical spectra based on nine different unit cells, as shown in Figure 6. There are four obvious absorption peaks in the theoretical spectrum of C1, which are 0.86, 1.16, 2.09, and 2.21 THz, respectively. There are six absorption peaks in the spectrum of C2, of which four absorption peaks are ap- parent, and two characteristic absorption peaks are weak. C3 has five obvious characteristic absorption peaks, located at 0.58, 1.15, 1.5, 1.99, and 2.35 THz. Compared with the other eight configurations, the theoretical absorption peak of C4 is the most, and there are seven absorption peaks. The absorption peaks of C5 are located at 0.55, 1, 1.33, 1.6, and 2.08 THz. There are four weak ab- sorption peaks in the theoretical spectrum of C6, which include one relatively strong absorption peak located at 2.28 THz. The number of theoretical absorption peaks of C7, C8, and C9 is 6, 5, and 5. There are significant differences in the theoretical spectra of different hydrogen bonding modes. This is because different hydrogen bond modes lead to different strengths of hydrogen bonds in the unit cell. The hydrogen bond itself is a noncovalent interaction. The stronger the noncovalent interaction, the more intense the intermolecular motion-induced (39).

FIGURE 6: The THz theoretical spectra of (a) to (i) as C1 to C9, respectively.

Moreover, the matching degree between theoretical and experimental spectra calculated by different hydrogen bonding modes are significantly different. As shown in Table II, the corresponding absorption peak numbers of the nine theoretical spectra are 3, 5, 3, 3, 4, 3, 4, 3, and 4, respectively. Based on the number of matching absorption peaks and the shifting degree of absorption peaks, it can be concluded that the hydrogen bonding modes of C2, C5, C7, and C9 are closer to the actual situation. It is worth noting that the N1–H1•••O4 hydrogen bond mode in C2 is theoretically the most consistent with the hydrogen bond of 5F-P.

The four systems of C2, C5, C7, and C9 have atoms. The covalent bonds in the unit cell are also the same. The difference among them lies in the mode of hydrogen bonding. Because the hydrogen bond is only a non-covalent interaction and not a chemical bond, its strength cannot affect the overall stability. Besides, a complete system always tends to the lowest energy because the lower the total energy of a substance, the weaker its ability to react. This substance can continue to exist in the most stable state. Therefore, the more stable a substance is, the lower the total energy it carries (40). In this study, the total energy of the four better configurations (C2, C5, C7, and C9) was calculated by PBE+Grimme to compare their stability and further verify the accuracy of the conclusion in section 4.5. Table III shows that the total energy is 2 < 7 < 5 < 9 before correction, and the total energy after dispersion correction is the same as 2 < 7 < 5 < 9. Therefore, the system with C2 as the unit cell is the most stable. The intermolecular hydrogen bonding mode of 5F-P is most inclined to C2. This result is consistent with the results of THz spectroscopy.

The comparison of the theoretical and experimental spectrum of 5F-P, as shown in Figure 7. The absorption peaks of 0.98, 1.11, 1.32, 1.55, and 1.99 THz in the experimental spectrum correspond to 1.01, 1.13, 1.35, 1.55, and 2.2 THz of the theoretical spectrum. It shows that the theoretical spectrum calculated by C2 simulates all the experimental absorption peaks of 5F-P. However, the theoretical absorption peak at 0.63 THz does not appear in the experimental spectrum. The reason is that the effect of water molecules cannot be eliminated entirely from the actual process and the low signal-to-noise ratio (S/N) of the equipment. As shown by the black arrow, the absorption peak at too low or too high frequency is annihilated by noise, making it difficult to detect accurately. Except for the absorption peak at 1.55 THz, other theoretical absorption peaks are blue-shifted to varying degrees relative to the experimental absorption peak. The reasons can be primarily attributed to the difference between the experimental (293 K) and the simulation temperature (0 K) (41,42). The change in temperature affects the size of the unit cell (43). The peak position and peak width of the theoretical spectrum will change accordingly. Because of the thermal expansion effect, the intermolecular distance increases with the temperature increasing, as does the bond length. The thermal expansion of the crystal lattice weakens the strength of the intermolecular interactions, resulting in the red-shift of the absorption peak (44,45). On the contrary, the absorption peak is blue-shifted. Therefore, the temperature difference affects unit cell simulation. It makes the frequency position of each absorption peak fluctuate to a certain extent, which is directly manifested as the theoretical absorption peak of C2 moving to a higher frequency. Some vibration modes exhibit an obvious red-shift with the increase of temperature, while others are less affected by temperature (46). The theoretical absorption peak at 1.55 THz does not have a blue-shift. This may be because the absorption peak originates from intramolecular and intermolecular vibrations in the THz band. The weak interactions have little effect on the vibration mode at this frequency. Therefore, the change in temperature cannot significantly affect the position of this absorption peak. In addition, another reason is the difference between actual and theoretical crystal structure. The simulation is based on the perfect crystal structure. However, it is difficult to reach the ideal degree in the experiment (47–49). The temperature effect and sample difference make the crystal parameters of experiment and theory not match exactly. It is manifested directly in the difference between experimental and theoretical spectra.

FIGURE 7: The THz experimental spectra of (green) 5F-P, and (red) the theoretical spectra of C2.

Figure 8 shows the vibration modes of the theoretical absorption peaks in the unit cell, corresponding to 0.98, 1.11, 1.32, 1.55, and 1.99 THz of the experimental absorption peaks, respectively. The vibration of C2 at 1.01 THz is mainly caused by the librational vibration of 5-FU along the b-axis. The vibration at 1.13 THz is attributed to the strong reverse translational vibration of Pro and 5-FU. The vibration at 1.35 THz is similar to 1.01 THz, mainly caused by the librational vibration of 5-FU along the b-axis, but the one-sided translational vibration of the five-membered ring on proline at 1.35 THz is more obvious. The absorption peak at 1.55 THz, assigned in rotational vibration of the five-membered ring on proline. In addition, it can be seen that the hydrogen bond vibration of N1–H1•••O4 between proline and 5-FU is close to zero. This result proves that the theoretical absorption peak at 1.55 THz does not blue-shift because the weak interaction has little effect at this position. The vibration at 2.2 THz is caused by the translational vibration of proline and unilateral 5-FU in the same direction.

FIGURE 8: The vibration mode of C2 at (a) 1.01, (b) 1.13, (c) 1.35, (d) 1.55, and (e) 2.2. THz (Black arrows represent translational vibration, red arrows represent librational vibration and blue arrows represent rotational vibration). The color code for atoms is given in the figure.

In this work, an investigation was conducted into the hydrogen bonding modes of 5F-P by combining THz-TDS and DFT. We found that the THz spectrum of 5F-P in the range from 0.25 to 2.6 THz is significantly different from the two monomers. Based on NBO analysis and hydrogen bond network structure, we constructed nine stable theoretical configurations of 5F-P. The THz theoretical spectra of differ- ent hydrogen bond modes are obtained by unit cell simulation, and the theoretical spectra of configuration 2 is the most consistent with the experimental spectra. The total energy of configuration 2 is the lowest and the structure is the most stable, which was determined by calculating the total energy of the system. Therefore, it can be seen that the proline and 5-FU in the 5F-P are connected by N1–H1•••O4 hydrogen bonding based on THz spectroscopy and system stability. These results demonstrate that THz-TDS can well characterize the formation of new phases. The combination of THz-TDS and DFT can effectively study the connection mode of hydrogen bonds.

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. These authors contributed equally.

The authors declare no competing financial interest.

(2) T. Balakrishnan, S. Sathiskumar, K. Ramamurthi, and S. Thamotharan, 

(3) B. Ishimoto, K. Tonan, and S.-i. Ikawa, 

(4) S.E. McLain, A.K. Soper, A.E. Terry, and A. Watts, 

(5) R. Rai, S. Aravinda, K. Kanagarajadurai, S. Raghothama, N. Shamala, and P. Balaram, 

(6) S. Bronco, C. Cappelli, and S. Monti, 

(10) A.V. Belyakov, M.A. Gureev, A.V. Garabadzhiu, V.A. Losev, and A.N. Rykov, 

(11) C. Heidelberger, N. Chaudhuri, P. Danne- berg, D. Mooren, L. Griesbach, R. Duschinsky, R. Schnitzer, E. Pleven, and J. Scheiner, 

(12) H. Baba, K. Teramoto, T. Kawamura, A. Mori, M. Imamura, and S. Arii, 

(14) F. Lara-Ochoa and G. Espinosa-PÉRez, 

(15) P. Vishweshwar, J.A. McMahon, J.A. Bis, and M.J. Zaworotko, 

(16) G. Chałasiński and M.M. Szczȩśniak, 

(17) C.A. Franca, S.B. Etcheverry, R. Pis Diez, and P.A.M. Williams, 

(18) J.L. Finney, J.M. Goodfellow, P.L. Howell, and F. Vovelle, 

(19) Z.-P. Zheng, W.-H. Fan, H. Yan, J. Liu, W.-Z. Yang, and S.-L. Zhu, 

(20) A. Heinz, C.J. Strachan, K.C. Gordon, and T. Rades, 

(21) M. Walther, P. Plochocka, B. Fischer, H. Helm, and P. Uhd Jepsen, 

(22) M. Ge, H. Zhao, W. Wang, Z. Zhang, X. Yu, and W. Li, 

(23) Y. Du, Y. Wang, J. Xue, J. Liu, J. Qin, and Z. Hong, 

Spectrochim. Acta. A. Mol. Biomol. Spectrosc.

(24) D.G. Allis, J.A. Zeitler, P.F. Taday, and T.M. Korter, 

(25) P.M. Hakey, D.G. Allis, M.R. Hudson, W. Ouellette, and T.M. Korter, 

(28) C. Funaki, S. Yamamoto, H. Hoshina, Y. Ozaki, and H. Sato, 

(29) Q. Zhou, Y. Shen, Y. Li, L. Xu, Y. Cai, and X. Deng, 

(30) M.D. Segall, P.J.D. Lindan, M.I.J. Probert, C.J. Pickard, P.J. Hasnip, S.J. Clark, and M.C. Payne, 

(31) J.P. Perdew, K. Burke, and M. Ernzerhof, 

(32) M.D. King, W. Ouellette, and T. M. Korter, 

(34) C. Moisescu-Goia, M. Muresan-Pop, and V. Simon, 

(35) A.C. Jorgensen, C.J. Strachan, K.H. Pol- lanen, V. Koradia, F. Tian, and J. Rantanen, 

(36) M.D. King, W.D. Buchanan, and T.M. Korter, 

(37) J.J. Koenig, J.M. Neudorfl, A. Hansen, and M. Breugst, 

Acta. Crystallogr. E. Crystallogr. Commun.

(38) A.T. Hulme, S.L. Price, and D.A. Tocher, 

(39) L. Xu, Y. Li, Q. Zhou, and X. Deng, 

(40) B. Xiao, J. Feng, C.T. Zhou, J.D. Xing, X.J. Xie, and Y.H. Chen, 

(41) M.D. King, W.D. Buchanan, and T.M. Korter, 

(42) M.T. Ruggiero, J.A. Zeitler, and A. Erba, 

(43) A.D. Squires, A.J. Zaczek, R.A. Lewis, and T.M. Korter, 

(44) N. Laman, S.S. Harsha, D. Grischkowsky, and J.S. Melinger, 

(45) L.M. Lepodise, J. Horvat, and R.A. Lewis, 

(46) Y. Shen, P. Upadhya, E. Linfield, and A.G. Davies, 

This work shows that the combination of THz-TDS and DFT can provide an effective
method to investigate the connection mode of hydrogen bonds and thus aid in characterizing compounds such as this potential drug candidate.

The Hydrogen Bonding Mode of 5-Fluorouracil-Proline Co-Crystal (5F-P) Studied by Terahertz (THz) Spectroscopy and Density Functional Theory (DFT)

Tobias Konz of Nestlé Research, Lausanne, Switzerland, and various associates have developed and validated what they describe as a reliable, robust, and easy-to-implement quantitative method for multielemental analysis of low-volume samples. The inductively coupled plasma mass spectrometry (ICP–MS)-based method comprises the analysis of 20 elements (Mg, P, S, K, Ca, V, Cr, Mn, Fe, Co, Cu, Zn, Se, Br, Rb, Sr, Mo, I, Cs, and Ba) in 10 μL of serum and 12 elements (Mg, S, Mn, Fe, Co, Cu, Zn Se, Br, Rb, Mo, and Cs) in less than 250,000 cells, and involved the analysis of elemental profiles of serum and sorted immune T cells derived from tumor-bearing mice. The results indicate a tumor systemic effect on the elemental profiles of both serum and T cells. Konz and his colleagues believe their approach highlights promising applications of multielemental analysis in precious samples, such as rare cell populations or limited volumes of biofluids, that could provide a deeper understanding of the essential role of elements as cofactors in biological and pathological processes. Konz spoke to us about this work.

In your paper (1), you state that single-cell ICP–MS (sc-ICP–MS) is an emerging methodology that holds promise to enable multielemental analysis of low-volume samples in the future. Can you explain why you believe this?

In my view, sc-ICP–MS has become a hot topic in the field of elemental analysis, and rightly so. Scientists working in the field of bioanalytics applied this approach to look into the smallest units of life. As a matter of fact, most biochemical processes take place in cells. Elemental bioanalysis in cells makes it possible to obtain deep insights into cellular processes and the biological status of specific cells. In comparison, the analysis of bioliquids mainly provides insights on a systemic level of the corresponding organism. Over the last decade, many scientists applied ICP–MS to study the elemental composition in all kinds of biofluids and tissues. Because of the latest technical developments, it is now possible to achieve ever lower detection limits. Thus, it is now possible to examine individual cells and their elemental composition—at least partially. This may open a completely new field of application for ICP–MS and there are many scientists who are about to prove this.

Why is ICP–MS generally considered being the method of choice for multielemental analysis as compared to other ICP techniques (atomic emission spectroscopy [AES] or optical emission spectroscopy [OES]) or more traditional atomic absorption (flame atomic absorption [F-AAS] or graphite furnace atomic absorption [GF-AAS])? 

ICP–MS is unarguably the gold standard for elemental and especially multielemental analysis. This is mainly because of the fact that this technique is extremely versatile compared to ICP–OES/AES and atomic absorption spectroscopy. Most AAS devices apply element-specific radiation sources (hollow cathode lamps) that enable the detection of a single element at the same time. Although there are some commercially available AAS instruments that allow the detection of 10–20 elements simultaneously, sensitivity limitations hamper the analysis of most trace and ultratrace elements. ICP–OES allows multielemental analysis, is relatively easy to setup, and well-suited for high-throughput analysis—however, spectral interferences can be challenging. ICP–MS allows high-throughput, multielemental analysis with the lowest-possible detection limits. It offers a linear detection range of up to 10 orders of magnitude, enables isotopic analysis (isotope dilution analysis for quantification, control of spectral interferences), and it is possible to analyze any type of sample material (gases, liquids, solids). Furthermore, high-resolution separation techniques, such as high-performance liquid chromatography (HPLC), gas chromatography (GC), and capillary electrophoresis (CE), can be hyphenated to the ICP–MS. Overall, this makes ICP-MS the most powerful and versatile technique for elemental analysis.

What are some cancer-specific multielemental profiles that you have identified?

In the mouse cancer modes under evaluation, the presence of the tumor influences the serum concentration levels of various elements. Some elements, such as sulfur, calcium, manganese, zinc, strontium and molybdenum, were not affected significantly by the presence of the tumor. On the other hand, elements such as phosphorous and copper were increased in the serum samples of tumor bearing mice, independently of the tumor type. Iron, on the other hand, was depleted. These results seem to indicate that the presence of tumor systemically influences the concentration levels of some serum elements, independently from the tumor type. On the contrary, elements such as vanadium, chromium, bromine, rubidium, cesium and cobalt show trends for tumor-specific concentration changes. Furthermore, our results show that different immune T cells (CD4+ and CD8+) have specific mineral profiles reflecting their specific immune function. Moreover, the presence of a tumor seems to have an impact in the mineral contents of T cells. Indeed, we observed elemental modulations within CD8 cells that could be associated with presence of cancer, such as higher levels of magnesium and manganese and lower levels of iron, cobalt, and rubidium. These findings might indicate an increased metabolic need of immune cells for manganese and magnesium in the presence of a tumor.

What are the biggest challenges that you encounter in this analysis method?

While the analysis of small amounts of biofluids is quite straightforward (centrifugation to remove particles and a simple dilution step), the analysis of cells is more challenging. In fact, one hurdle we faced during method development was to remove the cell sorting buffer (containing a high concentration of various elements of interest) from the cells. As large volumes of this buffer are required for a single cell-sorting experiment (roughly 10 L in our case), it was not possible to prepare a buffer solution based on reagents which are suitable for trace elemental analysis. For a washing step, we applied filters or considered osmosis for buffer removal, but none of these sample preparation strategies proved to be suitable. Finally, applying a mild centrifugation step to form a cell pellet, followed by removing the supernatant with a pipette, provided satisfying results. However, the removal of a few microliters from an Eppendorf tube, and at the same time maintaining the integrity of a tiny cell pellet, remains the Achilles heel of the method, and requires some finesse and experience.

With such small samples, is contamination an issue?

Contamination is always a challenge in ultratrace elemental analysis. Acid-washing, working in a clean environment and under a fume hood are the minimum requirements to obtain high quality results. However, contamination is not more challenging in this method, as compared to most other ICP–MS based methods dealing with trace and ultratrace analysis. This is mainly due to the fact that a relatively moderate sample dilution of (1:10) and few sample preparation steps are applied. Contamination could arise, for example, from plastic materials. We mitigate this risk by applying an acid-washing step up front. Another often underestimated source of contaminants are chemicals and reagents applied during sample preparation. They often contain elements (for example, iron) that enter the sample in this way and thus mix with the analytes. Because of the inherent properties of elemental mass spectrometry, it is then no longer possible to distinguish easily between endogenous and exogenous elements. This can lead to an overestimation of the element concentration. In our study, we only apply buffer solutions and nitric acid during sample preparation. Thus, the overall contamination risk is relatively low.

Can you briefly summarize the results that you have achieved so far?

We have developed a quantitative analytical strategy that enables the analysis of 20 biologically relevant elements in low-volume samples (10 μL) of serum. By applying an optimized workflow, the method is also applicable for the quantification of 12 elements in a low number of sorted cells (250,000 cells). In our paper, we give detailed descriptions of the experimental setup. This enables scientist to apply the approach to study biological-relevant elements (major trace and ultratrace elements) in very small sample volumes. Applied to tumor cells, we could demonstrate that the presence of certain cancer types influences differentially the elemental composition of serum concentrations. Furthermore, we could show that different immune T cells (CD4+ and CD8+) have specific mineral profiles reflecting their specific immune function. Last but not least, the presence of a tumor effects the mineral composition of T cells.

I am now working in a company which is rather focused on elemental analysis is food and pharmaceutical products. However, my former colleagues are continuing our research work and other scientist are working on different approaches to study trace- and ultratrace elements in cells. I am confident that rather sooner than later we will see new findings in this field, both in pre-clinical and clinical studies.

1. T. Konz, C. Monnard, M. Rincon Restrepo, J. Laval, F. Sizzano, M. Girotra, G. Dammone, A. Palini, G. Coukos, S. Rezzi, J.-P. Godin, and N. Vannini, 

 is currently leading the Elemental Analysis group at UFAG Laboratorien (in Sursee, Switzerland), specializing in quantitative analysis of elements in food and pharmaceutical products (GMP and ISO environment).

In precious samples, effective methods for multielemental analysis could provide a deeper understanding of the essential role of elements as cofactors in biological and pathological processes. Tobias Konz of Nestlé Research explains.

Jerome Workman Jr. serves on the Editorial Advisory Board of Spectroscopy and is currently with Unity Scientific LLC. He is also an adjunct professor at Liberty University and U.S. National University. He can be reached at jworkman04@gsb.columbia.edu.

) nanospore sensors can be used to analyze natural and synthetic oligosaccharides and polysaccharides like the anticoagulant drug heparin. SiNx sensors are providing an understanding of nanopore electrokinetics–mechanisms important for capillary electrophoresis with often outsized importance on the nanoscale. Recent work in nanopores is providing a platform for new assay development applicable to clinical analysis for many therapeutic molecules. Recently, we spoke to Jason R. Dwyer of the University of Rhode Island and a Federation of Analytical Chemistry and Spectroscopy Societies (FACSS) Innovation Award winner from the 2019 SciX conference, regarding his work in this field.

In a recent paper, you discuss the complexities of therapeutic oligo- and polysaccharides (1). Characterizing polysaccharides is a daunting task for conventional chemical analysis, as details such as contamination, monomer composition, stereo-chemistry, polymer length, monomer sequence, polymer branching, and monomer linkage types are required to understand the structure and function of these molecules. Where did you have the initial idea to investigate nanopore sensors for polysaccharide characterization, rather than typical separation methods combined with mass spectrometry? How did you decide to investigate polysaccharides, and specifically heparin?

When I started my independent academic career, the development of nanopore-based sequencing of DNA was in full swing, and was being pushed by an intrepid few toward protein analysis. I remember reading about the heparin crisis and thinking that on some level (a very simplistic level, mind you), that the heparin molecule is just a charged biopolymer like DNA, and that we might be able to apply the single-molecule sensing performance of nanopores to a pressing and important problem. What differentiates nanopores in this application compared to more sophisticated conventional chemical analysis tools is that they hold the promise of low-cost, high-performance sensing in a platform that relies on a very basic principle of operation. While simple in principle, it took a lot of development effort to bring the idea to fruition. Really talented students doing this hard work afforded me the luxury of considering what I wanted to do in the nanopore world. I had always been interested in turning nanopore sensors into sensors that were general and not restricted to only a particular class of analyte. At the same time, I reflected that my training as a chemist had helped me make unique contributions to my past research in ultrafast science–developing femtosecond electron diffraction and exploring the hydration dynamics of DNA by femtosecond infrared spectroscopy. It’s probably fair to say that if you can make a nanopore carbohydrate analyzer, you’re not too far away from a general chemical sensor platform. Given the importance of carbohydrate analysis, and the opportunities I saw from a chemistry and materials science perspective, it seemed like nanopore carbohydrate sensing was a very exciting and promising area on which to focus.

How is your work on nanopore sensing different from what others have done previously?

The work for which we won the FACSS Innovation Award involved discovering and optimizing a way to chemically tune nanopore sensing performance. Early demonstrations in the field required special materials or significant expertise to surface coat nanopores. In our paper, we functionalized more than 100 pores. I think more than had been reported functionalized in a decade of thin-film, solid-state nanopore literature. I can best summarize our approach as “bulletproof.” It opens up the palette of organic chemistry to nanopore science in a very straightforward way.

In the domain of nanopore carbohydrate sensing we were the first to do two things: First, we used thin-film nanopores made with a material that is ubiquitous in consumer electronics. Using a conventional micro- and nanofabrication material means fewer barriers to commercialization of our discoveries. Second, getting reliable tools into the hands of end users is a focus of our work, and therefore, we performed a survey study of a range of carbohydrates exhibiting properties that both revealed key mechanisms to exploit and showed that nanopore sensing could be optimized to cope with the incredible diversity of carbohydrate properties. This carbohydrate sensing work underscored the importance of our work on chemically functionalizing nanopores; the native nanopore composition, alone, would be hard-pressed to cope with the chemical diversity of carbohydrates.

In your nanopore sensing device you use a voltage-driven passage of a sample molecule that can pass into, across, or through an electrolyte-filled nanopore. Would you explain how this device can be used for analyte detection and characterization? 

The passage of electrolyte ions through the nanopore gives a steady “open pore” current whose magnitude is determined by the size and surface chemistry of the nanopore, the electrolyte composition, and the applied voltage. When an analyte passes into the nanopore (or near it), it displaces those electrolyte ions, giving rise to a current perturbation whose magnitude is dependent on those same parameters, plus size and charge distribution of the analyte. When the nanopore is small enough to force a biopolymer to pass linearly through a nanopore, each monomer constrained in the nanopore will contribute uniquely to the overall current perturbation. In the nanopore DNA-sensing world especially, considerable effort has gone into pulling out this sequence-specific information from the total signal.

Is the device and technology associated with nanopore sensors mostly applicable to spectroscopic applications, or could it also be used in conjunction with separation methods, such as chromatography or sample partitioning for mass spectrometry? Do you foresee nanopore devices being designed for specific assays and providing specific tests for low cost, yet accurate analysis of therapeutic molecules? 

I think that nanopores have a tremendous amount of potential in a stand-alone sense, especially when suitably chemically tuned to optimize sensitivity and selectivity for a particular analyte. They offer single-molecule sensing without the need for chemical labelling or spectroscopic readout since the properties the nanopore is sensitive to—size and charge—are inherently their own labels. Of course, nanopore sensing could be implemented after a chromatographic separation step, thereby increasing the selectivity. Nanopore sensing can also be used in conjunction with fluorescence detection, or with electron tunneling detection, for example. More broadly, nanopores can trap and manipulate single molecules in solution, and this capability can be exploited in a number of ways, and in combination with a number of other analysis tools. Nanopores already have a very strong foothold in DNA sequencing, and I can imagine that they will find application in a wide range of other sensing areas. We are really optimistic about their use for glycomics.

In another paper (2), you discuss nanofabricated thin-film platforms and their analytical applications using techniques such as visible or infrared spectroscopy, electron energy loss spectroscopy, X-ray photoelectron spectroscopy, and single molecule sensing. In this paper, your focus was on the thin film structure being common across platforms, rather than the nanopore being a focal element. What are the required differences in the nanopore sensor device itself when applied to these different analytical methods, and how can the nanopore devices be extended for imaging applications?

To specifically address your question, we craft well-defined, near molecular-sized channels through thin films of silicon nitride to create optimal nanopore sensors. Those channels can be used for nanopore-based sensing, to hold single molecules for study by other techniques, or as well-defined access ports to the contents of a sample cell bounded by the thin film. For conventional nanopore single-molecule sensing, the diameter of the nanopore should in general be not much larger than the dimensions of the conformation of interest of the given analyte. These same films that house the nanopores, when left intact, can serve as ~10–100 nm-thick windows for sample chambers that allow interrogation with a variety of techniques using optical and charged-particle beams. When even this sample wall thickness is too thick, well-defined access ports can be formed through the film-on the same scale or larger than the nanopores used for sensing in order to permit access to the underlying sample.

What are the main sampling limitations, if any, when using these nanopore sensing devices?

Broadly speaking, the nanopore sensor will detect every single molecule that passes through the pore. The trick is to make sure that the molecules of interest make it to the nanopore in a reasonable amount of time, an area benefiting from significant research effort. In nanopore DNA sequencing work, electrophoresis has been the easiest way to control the biopolymer motion. In our work on carbohydrates, the possibility of charge-neutral biopolymers has eliminated electrophoresis as a possibility, and we are relying on electroosmosis.

Nanopore sensing is not limited to solution-phase samples, although that is how the majority of the studies are carried out. In leaving the solution phase behind, the role of the nanopore might well change to be a single-molecule orifice to inject sample into a differ- ent type of detector, for example. But even in the solution domain, it’s really interesting to think about what the insolubility of a sample might mean for a detector that simply relies on passage of a molecule through a channel.

We are pushing onward in using chemically tuned nanopores to advance nanopore carbohydrate analysis, carrying out fundamental work and trying to develop applications. Our recent “ABC Highlights” article outlines our perspective on this question of the next steps (3).

1. B.I. Karawdeniya, Y.M.N.D.Y. Bandara, J.W. Nichols, R.B. Chevalier, and J.R. Dwyer, 

3. J.T. Hagan, B.S. Sheetz, Y.M.N.D.Y. Bandara, B.I. Karawdeniya, M.A. Morris, R.B. Chevalier, and J.R. Dwyer, 

 is a Professor of Chemistry at the University of Rhode Island. His research is at the intersection of bioanalytical chemistry and nanofabrication, with a focus on developing enabling tools for discovery and application.

Jason R. Dwyer of the University of Rhode Island discusses the application of nanopore sensors to the analysis of natural and synthetic oligosaccharides and polysaccharides.

Using Nanopore Sensors to Analyze and Characterize Heparin and Other Therapeutic Polysaccharides: An Interview with Jason Dwyer

Raman spectroscopy has been applied to study a range of chromites (Fe

 with variable chromium to aluminum to iron contents to ascertain conclusions about possible correlations between the chromium concentration and the position of the main Raman peaks within this mineral. Our intention was to examine chromite grains from different paragenesis, which vary significantly in their chromium content, to observe changes in the Raman spectra of this mineral. It was found that a negative correlation exists between the chromium number, calculated from the electron microprobe data, and the Raman peak number. Chromite grains with high chromium numbers show a low Raman peak, whereas samples with low chromium number show a higher Raman peak. Therefore, it is possible to infer a relationship between the mineral composition and Raman bands for this type of spinel. The measurements have clearly shown that it is possible to draw precise conclusions about the chromium content of the mineral based on the Raman peak alone. This finding supports a possible application of portable Raman devices on Earth or in space for asteroids and planets.

Raman spectroscopy has gained increasing importance in almost all areas of earth and material sciences. This is particularly true in studies of mineralogy, petrology, and volcanology. Raman spectroscopy has also gained increased importance in studying the geology of ore deposits and extraterrestrial materials, where both qualitative and quantitative estimates of the mineral content are essential. Moreover, it is emerging as a powerful and critical tool for analyzing the structural, chemical, and physical properties of crystalline solids, liquids, and glassy materials in a very effective and mostly nondestructive way (1–4). In earth science research, Raman spectroscopy provides simple and inexpensive utility in the characterization of structural modifications, elemental concentrations, or polytypism, and polymorphism in some of the major rock-forming minerals (3, 5, 6). For example, Raman peak shifts can be used to correlate spectral and chemical features, either in the laboratory or in the field (with portable Raman devices). In fact, given its versatile character, it is conceivable that one could construct miniaturized Raman systems that could readily be installed in planetary “rovers,” thereby facilitating quantitative surface analyses of other terrestrial planets or small bodies (4,7). However, to convert the Raman results into quantitative information, it is sometimes necessary to compare those results with compositional analysis achieved using other methods of mineral analysis (such as electron probe microanalysis [EPMA] or X-ray diffraction analysis [XRD]).

In this study, 10 highly chemically variable chromite grains (S1–S10) obtained from ophiolites, serpentinites, and a range of ore bodies here on Earth (Table I) were analyzed by micro-Raman spectroscopy to explore the relationships between Raman peak shifts and mineral chemistry, as determined by EPMA.

The chromium, aluminum, and iron content of the chromite was defined and correlated in various ways. For the calculation of the investigated chromites, we used the general formula for this mineral, (Fe

, and calculated the chromite number (#Cr), as:

Chemical and Spectral Features of Chromites

The most important host rocks for chromites are ultramafic mantle rocks, such as the obducted peridotite and serpentinite bodies found within ophiolite complexes (8–10). Chromite may also be found in ultramafic igneous rock sequences, in ore complexes, or in metamorphic terrains. Because of its comparatively high hardness, density, and stability, this mineral can also be found in the heavy mineral fraction of sedimentary rock within basinal sequences.

Chromite belongs to the spinel group of minerals (11), where magnesium frequently substitutes for iron (2+) and chromium substitutes for aluminum or iron (3+) in the crystal lattice. The peak shift in chromite is a function of relative proportions in chromium and aluminum (iron). Chromite has a spinel-type structure AB

 belonging to the Fd3m space group, where two formula units (Fe

comprise its primitive unit cell (12, 14, 15). Normal spinel and chromite have a cubic-closed packing structure, where oxygen anions, along with trivalent metal cations (chromium, aluminium, and ferric iron), occupy half of the octahedral interspaces, and other divalent cations (ferrous iron and magnesium) occupy one-eighth of the tetrahedral interspaces (12).

Figure 1 shows a typical Raman double peak for chromite. Two clear principal peaks can be observed at 555 cm

 (with a margin of error of less than ±1 cm

), and represent the main distinguishing feature in the identifying chromite by Raman spectroscopy. The typical Raman spectral pattern, as determined for end-member chromite, consists of a major broad peak near 685 cm

 with a weak shoulder, and a secondary peak near 650 cm

. It has been shown that the strongest peak (in this work ascribed as peak I) may vary between 678 cm

 (Table I), with its assignment to the A1g mode (14). This feature presumably is generated by the bonds in the (Cr

) 6-compound octahedron. Figure 2 shows how the weaker peak (herein peak II( may also vary its position from 529 to 601 cm

. Peak II could be assigned to F2g mode (13).

FIGURE 1: A typical Raman spectrum for chromite. It is obvious that chromite has two clear principal peaks, which are labelled as peak I (with a peak number of 687 cm

) and peak II (with a peak number of 555 cm

). Both peaks have an error of less than ±1 cm

. Peak I displays a small peak at the left shoulder to which less attention is focused; it was shown not to be important for our results.

This study focused on the positions of peak I and peak II for chromite minerals and, where relevant, identified the displacement of these peaks relative to endmember chromite. It became clear that the shift of these two peaks reflects the chemical composition of the respective chromite mineral (Figure 2). The position of the small shoulder, which partly occurs on peak I, at lower wavenumbers than the peak maximum was not included in this analysis.

FIGURE 2: Shows one Raman chromite measurement each of the 10 different samples (S1 to S10) measured. It can be clearly seen that both peak I and peak II shift. This shift of the main peaks is dependent on the Cr to Al to Fe ratio. The range of scatter values varies from 529 to 601 cm

 for peak II, and from 678 to 727 for peak I. The measurements reveal a significant shift that is coupled to a change in mineral chemistry, that is the composition of the various chromite samples.

For EPMA measurements, we utilized a Cameca SX 100 electron probe that was equipped with five wavelength-dispersive spectrometers and an advanced micro-beam automation for the EPMA analyses. The probe was also equipped with a back-scattered electron (BSE) detector.

For the analyses, an accelerating voltage of 15 kV was applied with a beam current of 20 nA and a nominal beam diameter of 1 μm. A range of natural minerals and synthetic samples were used as standards for crystal calibration and peak assignments: Wollastonite was the standard for calcium and silicon, synthetic nickel oxide for nickel, periclase for magnesium, sphalerite for zinc, corundum for aluminum, hematite for iron, escolaite and chromite for chromium, ilmenite for manganese and titanium, albite for sodium, and orthoclase for potassium. Matrix correction followed the procedures outlined in the literature (15,16). The reproducibility of standard analysis was <1% for each element routinely analyzed. The measurements were performed on polished grain mounts of chromite embedded in epoxy; samples were coated with a thin carbon layer.

Raman spectroscopic investigations were carried out using a XploRa One micro-Raman instrument (Horiba Jobin Yvon) operating with the software LabSpec5. The spectrometer was equipped with edge filters, a Peltier cooled CCD detector, and three different lasers, working at 532 nm (green), 638 nm (red), and 785 nm (near-IR), respectively. To characterize the chromite crystals, a green 2ω – Nd:YAG laser (532 nm) was used in an attenuated mode (50% laser power), belonging to 5.5 + 0.1 mW on the sample surface. Focusing was completed through a 100 x LWD (long working distance) objective, resulting in a 0.9 μm laser spot-size on the sample surface. The wavelength calibration of the green laser was performed by manual calibration with a pure silicon wafer chip. The main peak intensity was centered interval 520 cm

. The wavenumber reproducibility was checked several times throughout the analytical runs, providing for a deviation of <0.2 cm

. Monthly deviation for this set up was in the range of 1 cm

 before calibration. The hole and slit openings were set to 300 and 100 μm, respectively. A 1800-T grating was used to obtain better spectral resolution. Short counting times (2 x 8 s) were chosen to minimize the heating or oxidation effects of the chromite specimens. The precision for determining the Raman peak positions by this method is estimated to be ±1 to ±1.5 cm

Within a single Raman spectroscopic measurement, the composition of a mineral is not measured directly but can be extracted by Raman peak position shifts (3,14,17). The major Raman peak position of a particular mineral of interest is subjected to systematic and measurable peak shifts in the order of 2–15 cm

. Such peak shifts can subsequently be used to calculate the cationic ratios through comparison of the minerals chemical composition, typically determined by EPMA. The errors of the Raman system are very low, usually on the order of ±.5 cm

. Because the Raman system errors are low, it is feasible to use Raman spectra to identify and characterize the peak shift, and to correlate this with mineral chemistry and composition. Thus, we may use this method to help establish a correlation between oxide concentration and Raman peak position (Table I and Figure 3) in minerals such as chromite. Thanks to this tool, future Raman spectroscopic measurements will allow for the rapid determination of chromite chemistry.

Raman measurements of chromite minerals demonstrated that chromium content could be accurately determined, supporting a possible application of portable Raman devices on Earth or in space for mineral analysis of asteroids and planets.

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