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# Genericity versus expressivity - an exercise in semantic interoperable
research information systems for Web Science

Christophe Guéret

Tamy Chambers

Linda Reijnhoudt

Frank van der Most

Andrea Scharnhorst

Data Archiving and Networked Services (DANS)  Den Haag, The Netherlands
<EMAIL_ADDRESS>School of Library & Information Sciences
Indiana University, USA<EMAIL_ADDRESS>Data Archiving and Networked
Services (DANS)  Den Haag, The Netherlands<EMAIL_ADDRESS>Data
Archiving and Networked Services (DANS)  Den Haag, The Netherlands
<EMAIL_ADDRESS>Data Archiving and Networked Services (DANS)
Den Haag, The Netherlands<EMAIL_ADDRESS>
###### Abstract

The web does not only enable new forms of science, it also creates new
possibilities to study science and new digital scholarship. This paper brings
together multiple perspectives: from individual researchers seeking the best
options to display their activities and market their skills on the academic
job market; to academic institutions, national funding agencies, and countries
needing to monitor the science system and account for public money spending.
We also address the research interests aimed at better understanding the self-
organising and complex nature of the science system through researcher
tracing, the identification of the emergence of new fields, and knowledge
discovery using large-data mining and non-linear dynamics. In particular this
paper draws attention to the need for standardisation and data
interoperability in the area of research information as an indispensable pre-
condition for any science modelling. We discuss which levels of complexity are
needed to provide a globally, interoperable, and expressive data
infrastructure for research information. With possible dynamic science model
applications in mind, we introduce the need for a “middle-range” level of
complexity for data representation and propose a conceptual model for research
data based on a core international ontology with national and local
extensions.

###### keywords:

Ontology; VIVO; NARCIS; CERIF; CRIS; Linked Data

###### category:

I.2.4 Knowledge Representation Formalisms and Methods

##  1 Introduction

The science of the 21st century, to a large extent is team science [10],
operating globally, often cross disciplinary, and fully entangled with the
web. The study of science as a specific, complex, and social system has been
addressed by many research disciplines for quite some time. The availability
of digital traces of scholarly activities at unknown scale and variety,
together with the urgent need to monitor and control this growing system, is
at heart of knowledge economies and has brought the question how best to
measure, model, and forecast science back on to the research agenda [32].

When reviewing the current models of science, it is clear there is no
consistent framework of science models yet [7]. Existing models are often
driven by the available data. For example, interdisciplinary bibliographic
databases (such as the Web of Science or SCOPUS) use the principle of citation
indexing [17] from the field of scientometrics to analyse the science system
based on formal scholarly communication. Typical output indicators are counts
of publications, citations, and patents. They form the heart of the current
“measurement of science” and have been taken up as data by network science [5]
and Web Science [23].

This specific kind of output is, however, only a tiny fraction of information
on science dynamics. Traditionally, the measurement of science encompasses
input indicators (human capital, expenditure), output indicators, and. where
possible, process information [18]. Research Information Systems, around since
WWII in Europe, are marking the shift to “big science” [29]. However, the
input side to science dynamics, in particular researchers, has been
underrepresented in quantitative science studies for quite some time. This is
partly due to the lack of databases and the problem of author ambiguity in the
existing database [33, 30]. Information on researchers has been mainly
collected, documented, and curated locally at individual scientific
institutions - and in nation-wide research information systems, at least in
European countries.

The emergence of the web has transformed this situation completely. The web
has become an important, if not the most important, information source for
researchers and a platform for collaboration [6]. The extent and diversity of
the traces scholars leave on the web has called for alt metrics [39]. It has
also triggered the development of standards and ontologies capable of
automatically harvesting this wealth of information, beyond existing
traditional bibliographic reference.

The wealth of information provided on the web about researcher activities and
their relations carries the potential for new insights into the global
research landscape. But we are not yet at the point where this data can be
both expressive enough to be useful and easy enough to consume.

To illustrate the current situation we display the conceptual space of
communities dealing with research information in form of four mind maps (c.f.
Figure 1). In the upper left corner we brought together concepts, which are
relevant from the perspective of scientific career research and often
conducted qualitatively, with rich factual evidence, which is hardly
interoperable or scalable. For this mind node we drew on current discussions
and first results [37] in a FP7 framework programme ACUMEN, Academic Careers
understood by Measurements and Norms (see http://research-acumen.eu/), where
sociologists and scientometricians work together. In the right lower corner we
display the main classes of an ontology for research information
(VIVO111http://www.vivoweg.org) developed in the US. In the upper right
corner, the main tables of a Dutch Research Information Database (NOD-NARCIS)
are displayed, and in the lower left corner is a selection of information and
concepts which can be retrieved using different fields in one of the leading
cross-disciplinary bibliographic databases - the Web of Knowledge. Although,
the mind map sketches are different in nature, from formal schemes to
collection of aspects, this illustration shows their difference in size,
granularity, scope, and expression or semantics.

Figure 1: Conceptual space of four different communities dealing with research
information. The variation among these mind maps illustrates the difference in
size, granularity, scope, and expression of the different information systems
with which they are associated.

In this work we argue for the need of a scalable, interoperable, and multi-
layered data representation model for research information system (RIS).
Science of science and modeling of science dynamics raise and fall with a
consistent measurement system for the sciences. The contributions of this
paper are as follows:

  * •

A highlight of information loss happening when expressing data with generic
ontologies;

  * •

The introductions of the notion of levels of semantic agreement for expressing
research data;

  * •

A multi-layered ontology based on the above definition.

The remainder of the paper describes the landscape of research data
publication before diving into the details of a specific Dutch case. We
thereafter introduce our proposed multi-layer conceptual model for a research
ontology and conclude in its potential for documenting research.

##  2 Current landscape of RIS

###  2.1 Publishing research data

In order to publish re-usable research data, one has to think in terms of
standards and publication media. While the web imposes itself as the
publication platform, the question of standards remains open and has been long
investigated.

First efforts in standardisation have been undertaken from the traditional
research information communities. One example is the “CERIF” standard
developed by EuroCRIS222http://www.eurocris.org. This standard defines a set
of generic classes and properties used to describe research data. The
serialisation format used for the data is XML, although an RDF version is
being considered333http://spi-fm.uca.es/neologism/cerif. The content
management system (CMS) “METIS”, popular in the Netherlands, uses this
standard to store and expose research data. This standard has also been used
for the Dutch portal “NARCIS”444http://www.narcis.nl/.

The Web of Linked Data is a way of combining the publication platform and the
standards. More recent efforts have been made in this direction via a number
of ontologies and publication platforms. The initiative
LinkedUniversities555http://linkeduniversities.org/lu/ provides a reference
towards these systems and highlights their practical use. VIVO a United States
based open source semantic web application is another such a system. The
application both describes and publishes data, using RDF to encode the data
and OWL for the logical structure.In addition to its own classes and
properties, the VIVO ontology incorpates other standard ontolgies thus
increasing its interoperability [8]. However, the ontology relies heavly on
the US academic model which limits its ability to accurately represent
researchers in other systems.

VIVO and CERIF based CMS have been successfully put in use at many
institutions. Still, the landscape of research information is very scattered
and far from being connected. One of the reasons for this is a lack of
agreement upon semantics for the data. Efforts have been made to align VIVO
and CERIF [25] but the main problem remains that data publishers essentially
have to choose between using a globally agreed upon representation, which is
less expressive as a result of covering a vast amount of heterogeneous
information (CERIF), or a very expressive and specialised ontology (VIVO),
which is difficult to map to other ontologies of similar complexity.

###  2.2 The Dutch case

In the Netherlands, we find the following situation. All 13 universities (14
with the Open University) use a system called METIS to register and document
their research information [14]. In practice, information is usually entered
in METIS centrally by a person in the administration although, sometimes
individual accounts to METIS are created. Aside from those unconnected local
implementations of one system, higher education in the Netherlands embraced
the Open Access Movement with a project called DARE. This lead to an open
repository for scientific publications. Moreover, a web portal to Dutch
research information exists - NARCIS - which harvests publications from open
repositories, but also entails a very well curated (and still manually edited)
research information database (NOD) with information about the scientific
staff of about 400 university and outside university research institutions
[13, 31].

As Oskam and other Dutch researchers already pointed out in 2006, “the
researcher is key” [27]. Outside of institutional RIS this idea is prolific in
Web 2.0. platforms such as Mendeley and Academia.edu. They have been designed
around the needs of scholars. General social network sites such as LinkedIn -
which is very popular for professionals in the Netherlands - and Facebook also
profile themselves as outlets for individual researchers. This leads to a
situation where user-content driven systems compete for the limited time and
resources of an individual researcher and where, as a result, snippets of the
oeuvre and academic journey of a researcher can be found at different places,
recorded in different standards, and with different accuracy. The question
raised in the 2006 paper: “How can we make the CRIS666CRIS stands for “Current
Research Information System” a valuable and attractive (career) tool for the
researcher?” [27, p. 168] is still waiting to be answered in a standardized
way.

The purpose of documentation of science (and of careers of researchers) has
grown far beyond the effective information exchange. Research evaluation
relies heavily on indicators computed (semi) automatically from databases and
the web. Currently, individual careers of researchers are very much influenced
by indicators which are built on activities for which large amounts of
standardised data are available. Prominent examples are journal impact factor
or the H index. But, a researcher is not just a “paper publication machine”.
Grant acquisition is another important “currency” in the academic market - for
individuals on the job market, as well as, for institutions competing for
funding. Teaching is an area which is monitored locally and institutionally,
but for which no cross-institutional databases exist. Moreover, researchers
are no longer loyal to one institution, one country, or one discipline for
their whole life. There is an increasing need for cross-discipline and cross-
institutional mapping of whole careers.

###  2.3 Tracing scientific careers

Projects such as ACUMEN look into current practices of evaluation and peer
review to empower the individual researcher and develop guidelines for how
best to present your academic profile to the outside world. “ACUMEN” is the
acronym for Academic Careers Understood through MEasurements and Norms. In
this project, we analyse the use of a wide range of indicators - ranging from
traditional bibliometrics to alt-metrics and metrics based on Web 2.0 - for
the evaluation of the work of individual academics. One of the author of the
present work, Frank van der Most, also conducted interviews to investigate the
impact or influence of evaluations on individual careers. For his work the
following events are of interesting in tracking an academic career:

  * •

Birth of the academic;

  * •

Acquisition of diploma’s and titles, in particular MA diplomas (and
equivalents), PhD/Dr. diplomas, habilitiation, professorships of sorts and
levels;

  * •

Jobs, in universities and academic research institutes, but also in non-
academic organisations. The latter is interesting because people move in, out,
and sometimes back into academia;

  * •

Particular functions within or as part of the job(s): director of studies
(teaching), research-coordinator, head of department, dean, vice dean (for
research, education, or other), vice-chancellor/rector, board member of
faculty/school/university/institute;

  * •

Launch of start-ups/spin-outs or people’s own companies. It could simply be a
form of employment, but start-ups or own companies may indicate economic or
other societal value of academic work;

  * •

Prizes;

  * •

Retirement and decease.

For the study of the impact, or influence, of evaluations an overview of
someone’s career is necessary to “locate” influential evaluations. This
“location” has multiple dimensions. One is the calendar time, i.e. on which
date or in which year did an influential evaluation take place. Based on time,
geographic, and institutional location the context of a particular evaluation
event can be reconstructed. Scientific careers follow patterns which are
influenced by current regimes of science dynamics (including evaluations).

Another important dimension concerns the location of an evaluation (or any
event) within someone’s career. If two academics apply for the same job, the
location in time and place is the same, but if one is an early-career
researcher and the other is halfway through his/her career, this clearly makes
a large difference to how their applications are being evaluated and how the
evaluation results are likely to impact their respective careers. A rejection
may have a bigger impact on the early-career researcher than on the mid-career
researcher.

Another ACUMEN sub-project investigates gender effects of evaluations and
includes an analysis of performance indicators on research careers. This is
planned to be a statistical analysis which would require some form of career
descriptions.

One of ACUMEN’s central aims is to identify and investigate bibliometric
indicators that can be used in the evaluation of the work of individual
researchers. A major point discussed in the ACUMEN workshops is the
realisation that researchers have a career or a life-cycle which
contextualises the values of bibliometric indicators.

Although the events listed above are interesting for ACUMEN, these events, or
a sub-set or extension thereof, is likely to be interesting to many career
studies. For example, productivity-studies would relate academic production of
texts [11, 15, 24], courses taught, and other outputs to someone’s career
stage or career paths. An academic’s epistemic development (their research
agenda) could be studied in relation to career stages [22] or mobility.

To be able to trace the co-evolution of individual career paths and the social
process of science for larger part of science, one would need a different kind
of information depending on the study being undertaken.

##  3 Towards a core research vocabulary

The challenge when designing a standard for sharing data is to make it generic
enough so that aggregation makes sense, while being specific enough so
institutions can express the data they need.

As it is highlighted by the two most popular search tools, consuming data
exposed via VIVO from a number of external sources777See
http://nrn.cns.iu.edu/ and http://beta.vivosearch.org/ at the international
level, only the most general concepts such as “People” make sense. On the
opposite, the search features offered by a national portal such as NARCIS
proposes a number of refined search criteria. These two extremes of the data
mash-up scale show that depending on the study being done, different levels of
semantics agreement are likely to be put into use.

In contrary to XML schemas, Semantic Web technologies make it possible to
express data using an highly specified model while also making it available
using a more general model. The technology of particular importance here is
“reasoning”, that is the entailment of other factual valid information from
the facts already contained in the knowledge base. For instance, if an RDF
knowledge base contains a fact assessing that “A is a researcher” and another
stating that “Every researcher is a person”, the system will infer that “A is
a person”.

Leveraging this, it is possible to extend ontologies by refining the
definition of classes and properties. The most refined versions of the
concepts will inherit from their parents. We argue that for research
information systems, three levels are necessary (see Figure 2). First, an
international level containing a set of core concepts that can be used to
build data mash-up on an international scale. Then, a national level extending
the previous core level with concepts commonly agreed upon nation wide (e.g.
positions). Last, an institutional level where every institution is free to
further refine the previous level with its own concepts and properties that
matter to its network.

Figure 2: The proposed model of multi-layer ontology and its trade-off between
scope and expressivity. At the lowest level, institutionaly defined semantics
have the highest expressivity but the lowest scope.

As a feasibility assessment and to propose a first model, we hereafter
introduce a core ontology and two national extensions. This proposal is based
on related work, existing ontologies, and our personal experience but stands
more as a first iteration of work in progress rather than a definitive model.

###  3.1 Conceptual models

Conceptual models allows for the representation of classes and properties of a
knowledge base, along with their relations, in an abstracted way. The proposed
conceptual models that we hereafter introduce are not dependent on the
technical solution implementing them. There is however, as highlighted
previously, an advantage in using Semantic Web technologies for this. This
point is discussed in details in the following, after the introduction and the
description of the three proposed conceptual models.

####  3.1.1 Core model

The model depicted in Figure 3 is a proposal for a core research ontology
based on the work being done on CERIF, the VIVO ontology, the Core
vocabularies [4], and the data needs of ACUMEN. As part of its goal to study
the scientific career through the research data made available, ACUMEN needs a
number of information related to individuals, such as but not limited to:

  * •

Grants/project applications - both applied and granted. This in relation to
persons (applicants of various sorts) and organisations (applying/receiving
institutes, main and sub-contractors, funding institutes);

  * •

Skills. For instance, “Leadership” or “Artificial Intelligence”. There is no
limit to the definition and several thesaurus could be implied;

  * •

Networks or network relations. Relation between persons and organisations, but
also between persons and results are of particular importance;

  * •

Memberships of scientific associations or academies;

  * •

Conferences visited or organised.

The model contains classes to define individuals, projects, scientific output,
positions and tasks. A generic “Relation” can be established between authors
and papers, or teachers and courses taught. The exact meaning of the relation
is to be defined either by sub-classes of it or by using the property “role”.

Figure 3: Conceptual model of the core ontology. This model describes the
minimum set of classes, relationships, and properties needed to describe a
natural person and trace his scientific career. These classes can be further
extended by national and local ontologies to account for specificity. As an
example, the coloured classes are extended in two national ontologies in
Figure 4

####  3.1.2 National extensions

The second level of semantic agreement is that of national extensions. Based
on the core concepts, these extensions allows for the modeling of concepts
actually used in the country - using the language and terminology of that
country. When building such an extension, the main assumption made is that
there is a level of agreement that can be reached on a national basis.

An example of national extension is given in Figure 4. This extension extends
the core “Position” and “Organization” classes to define the type of positions
and organisation commonly found in the Netherlands (Figure 4(a)) and the US
(Figure 4(b)). The classes depicted in the Dutch extension are those found in
NARCIS, and as such represent the union set of all the specific classes used
within the research institutions in the Netherlands888We must note here that
this classes are not defined by an authority but are rather crowd-sourced. A
more accurate, authoritative, list would have to be defined by an national
entity..

(a) Conceptual model of the extension for the Netherlands

(b) Conceptual model of the extension for the US

Figure 4: Example of two national extensions of the core model. These
extensions allow for expressing the particularities found in the national
system while grounding their semantic on the more generic concepts.

It can be observed that the Dutch extensions shows a high level of variety,
with some classes that could be replaced with other model mechanisms, such as
the “part time Hoogleraar” class which is actually a “Hoogleraar” contracted
with less hours.

We also note from Figure 4(b) that the national level has to be kept generic
in the US because of the variation observed locally. In the US, many titles
and/or positions are essentially at the discretion of the individual
institutions (with some direction from the American Association of University
Professors (AAUP)), thus a very detailed national ontology is not appropriate.
However, for countries with a more centralised model and using title and
positions officially described, more detail can be added at this level thus
increasing semantic understanding. The national level allows for this grey
area adaption instead of the current two level “very general” to “very
specific” model.

####  3.1.3 Local extensions

Local extensions are the most specific level of specification we propose for
this approach. These can be used to specify concepts and relations that are
understood within a given sub community inside a country. For instance, in the
Netherlands, the research institution KNAW defines an additional position
“AkademieHoogleraar” for “Hoogleraar” which are appointed to universities but
directly affiliated to KNAW. This additional position is only used by some
institutions and for this academy - here, the “Akademie” in
“AkademieHoogleraar” implicitly refers to KNAW.

###  3.2 Implementation

Prior to its concrete use, the proposed conceptual models have to be turned
into an RDF based vocabulary. This vocabulary also has to be hosted under a
domain name.

####  3.2.1 Vocabulary terms

There are a large number of vocabularies published on the Web. The proposed
models can effectively leverage most of their properties and classes from one
of these existing sources of terms, having fewer new terms to introduce. In
particular, the following vocabularies are to be considered:

  * •

FOAF999http://xmlns.com/foaf/spec/, for the description of the persons;

  * •

BIBO101010http://bibliontology.com/, for the publications;

  * •

LODE111111http://linkedevents.org/ontology/, for the description of events;

  * •

SKOS121212http://www.w3.org/2004/02/skos/, for the description of thesaurus
terms such as those used to describe researchers’ skills;

  * •

PROV-O131313http://www.w3.org/TR/prov-o/, to add additional provenance
information to the data being served.

We also note that, by design, there is a significant overlap between the
conceptual model of Figure 3 and that defined in the Core Vocabularies for
Person, Location and registered Organisations in [4, page 10]. This allows for
the proposed core vocabulary for research to be defined based on these other
core vocabularies defined by JoinUp and formalised by the W3C in the context
of the Working Group on Governmental Linked Data (GLD)
141414http://www.w3.org/2011/gld/wiki/Main_Page.

####  3.2.2 Ontology hosting

The domain name at which an ontology is being served is, as for the data
itself, often seen as indication of the person, or entity, in charge of
supporting the ontology. To account for this, we envision the hosting of the
core ontology and its extensions done at institutions matching the scope of
the level of agreement. That is, an international organisation for the
international layer, a national organisation for the national layer, and the
institutions themselves for the local extensions. More concretely, such an
hosting plan could be materialised as having: the core ontology being served
by the W3C, the Dutch national ontology by the VSNU151515the association in
charge of the collective labour agreement for Dutch universities and other
cross-institution regulations on salaries and positions, and the local
extension from the KNAW by the KNAW.

##  4 Conclusion

This paper operates at different levels. At the core it proposes a model to
semantically describe data in Research Information Systems in a way which
allows to aggregate but also to deconstruct if needed. It does so based on
experiences with standards and data representation in the past and looking
into very concrete practices - taking a VIVO implementation exercise in the
Netherlands as point of reference and departure. A next shell of
considerations around those specific mappings is added when we incorporate
research outside of the traditional area of scientific information and
documentation. Science and technology studies, science of science, and
scientometrics have produced over decades of insights in the structure and
dynamics of the science system. A wealth of information is available in this
area, most of it case-based evidence. We include the aims and achievements of
an on-going EU FP7 funded project (ACUMEN) which, in itself tries to combine
bibliometric and indicator-based research with interviews, survey, and
literature studies. The target subject of this project is the researcher. It
is also the researcher which is targeted by Research Information Systems, and
it is the researcher which is the innovative driver for science dynamics.
Bibliometric indicators are heavily based on standards, part of them shared
with RIS. What makes the ACUMEN project and the perspective of scientific
career research so interesting for the design of future research information
systems is the identification of factors relevant for career development which
are not yet covered by current standards, databases, or ontologies. The last
and most visionary shell in this paper is to design research information
systems which can be used for science modeling. In the general framework
developed by Borner et al. science models can be developed at different scales
of the science system, from the individual research up to the global science
system; they can differ in geographic coverage, as well as, in scales of time.
In any case, the ideal would be having one data representation which can be
scaled up and down along those different dimensions, and not singular data
samples in incomparable measurement units not relatable for particular areas
of the dynamics of science. Our main argument is to provide a data
representation which is retraceable - if needed - towards its specific roots
and at the same time can be aggregated. In such a “measurement system” we
would find a middle layer of data granularity on which basis complex, non-
linear models can be validated and implemented, to better monitor and
understand the science system.

##  5 Acknowledgments

This work has been supported by the ACUMEN project FP7 framework. We would
like to think our colleagues Ying Ding, Katy Borner, and Chris Baars for their
comments and support during this work.

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# Photoelectron circular dichroism upon multiphoton ionization of a chiral
alcohol

Peter Krüger  Radboud University, Institute for Molecules and Materials,
Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands  Michiel Balster
Radboud University, Institute for Molecules and Materials, Heyendaalseweg 135,
6525 AJ Nijmegen, The Netherlands  Bhargava Ram Niraghatam  Radboud
University, Institute for Molecules and Materials, Heyendaalseweg 135, 6525 AJ
Nijmegen, The Netherlands  Maurice H. M. Janssen  MassSpecpecD BV, 7522LL
Enschede, The Netherlands  Daniel A. Horke<EMAIL_ADDRESS>Radboud
University, Institute for Molecules and Materials, Heyendaalseweg 135, 6525 AJ
Nijmegen, The Netherlands

###### Abstract

We present the first photoelectron circular dichroism (PECD) measurements of
chiral alcohols, and in particular 1-Phenylethanol, using multiphoton
ionization at 400 nm. Observed PECD values were rather small at $\sim 2$%, but
could be reliably extracted using both hemispherical integration and Abel
inversion approaches. Experimental uncertainties of $<0.3$% (2$\sigma$) where
achieved with a collection time of around 2 hours. All experiments were
conducted in a new compact spectrometer, featuring a continuous flow
supersonic expansion and velocity-map imaging detection. The latter is crucial
to extract reliable PECD values, as it allows discrimination of different
features in the photoelectron spectrum, which exhibit different and opposing
PECD signals. The use of a tabletop multiphoton universal ionization scheme is
an important step towards a viable analytical chiral spectrometer based on
PECD.

## Introduction

The phenomenon of chiral molecules, that is molecules with non-superimposable
mirror images, continues to fascinate scientists across a wide variety of
disciplines. From the inherent chirality in biomolecules, leading to
enantiomer-dependent bioactivities and the so-called _homochirality of life_
Mason (1984); Ozturk and Sasselov (2022), to fundamental physics and the quest
for parity violation in chiral molecules Tranter (1985); Fiechter et al.
(2022).

Along with the general interest in chiral molecules ran the development of new
chirally-sensitive spectroscopic techniques. While traditionally this was the
domain of dichroic absorption spectroscopies, such as electronic or
vibrational circular dichroism, several new approaches have emerged in the
last decades, offering much higher sensitivities and chiral responses Lehmann
et al. (2013); Patterson et al. (2013); Ayuso et al. (2022). The concept of
photoelectron circular dichroism (PECD) has been shown to be particularly
suitable. This involves measuring the direction of outgoing photoelectrons
following ionization with chiral (circularly polarized) light, with a PECD
effect manifesting itself as an asymmetry in the forward and backward emission
yield Janssen and Powis (2014). PECD measurements have emerged as an
attractive option for chiral analysis, since it frequently offers much higher
chiral responses (sometimes 10s of % Ganjitabar et al. (2018)) than
absorption-type spectroscopies, and can be combined with the high detection
efficiency and throughput of charged-particle imaging Chandler and Houston
(1987); Eppink and Parker (1997).

PECD was first theoretically described in the 1970s Ritchie (1976); Cherepkov
(1982), which was further refined in the early 2000s Powis (2000, 2000),
before the first experimental observation some 30 years after the initial
theoretical predictions. These first experiments utilized circularly polarized
vacuum ultraviolet (VUV) synchrotron light sources Böwering et al. (2001);
Garcia et al. (2003). Using single photon VUV photoionization for PECD
detection has been a thriving field ever since Nahon et al. (2015), nowadays
featuring dedicated endstations at synchrotron facilities Garcia et al.
(2009); Nahon et al. (2012). The realization of laboratory-based PECD
measurements was shown around 10 years later Lux et al. (2012); Lehmann et al.
(2013), utilizing femtosecond resonance-enhanced multiphoton ionization
(REMPI) and coincidence detection of the produced photoion and photoelectron
Vredenborg et al. (2008). This was followed by the demonstration of the chiral
analysis of complex mixtures using the same approach Fanood et al. (2015). The
laboratory-based PECD approach has also expanded significantly in the past
years Janssen et al. (2022), and recent technological demonstrations include
the use of nanosecond high-resolution REMPI as an ionization source Kastner et
al. (2020); Ranecky et al. (2022), high-harmonic based XUV light sources Ferré
et al. (2015), the incorporation of novel molecular sources Sparling et al.
(2023), and high-throughput experiments with fiber-based high repetition-rate
femtosecond lasers Comby et al. (2018, 2020). Chiral analysis using PECD has
also been demonstrated for photodetachment from gas-phase anions recently
Krüger and Weitzel (2021); Triptow et al. (2023), including the first
chirality measurement of larger biopolymers Krüger et al. (2022).

Figure 1: Structures of 1-Phenylethanol enantiomers.

In this contribution we report the first PECD measurements of the two
enantiomers of 1-Phenylethanol, as shown in Figure 1. These were performed
using femtosecond multiphoton ionization (fs-MPI) at 400 nm, and hence
demonstrating chiral analysis with a fully universal table-top ionization
approach. The resulting PECD was rather small, $\sim$2%, but could be measured
with an absolute error of <0.3% in a 2 hour long measurement with a 3 kHz
repetition rate laser system. These measurements were conducted on a new and
compact PECD spectrometer, specifically designed to be cost and space
effective, while still making use of the benefits of a continuous cold
supersonically-expanded molecular beam, and velocity-map imaging photoelectron
detection Eppink and Parker (1997). Since the spectrometer makes use of a
continuous molecular beam, the duty cycle is limited only by the repetition
rate of the laser and hence could be improved by 2–3 orders of magnitude by
the use of high repetition-rate fiber-based femtosecond light sources.

## Experimental Methods

Figure 2: Schematic of the experimental setup. Molecules are supersonically
expanded into a vacuum through a 15 µm orifice capillary, located 5 mm from
the interaction point at the center of a velocity-map imaging (VMI)
spectrometer. Molecules are ionized by circularly polarized 100 fs 400 nm
laser pulses, with the helicity controlled by a motorized quarter-wave plate.
Electrons or ions are accelerated by the VMI electrodes towards a position
sensitive detector, _via_ a $\mu$-metal shielded field-free flight tube. For
mass spectra the detector response is picked up by a constant-fraction
discriminator and time stamped by a time-to-digital converter. Photoelectron
images are recorded with a fast CMOS camera and centroided on-the-fly.

Experiments were conducted in a newly designed compact velocity-map imaging
(VMI) spectrometer, a schematic of the new experimental setup is shown in
Figure 2. This consists of two vacuum chambers, with differential pumping
provided by the VMI electrodes that separate the chambers. The compact source
and interaction chamber is based on a CF63 6-way cross and houses the VMI
electrodes and gas nozzle. It is pumped by a small turbomolecular pump (HiPace
300, Pfeiffer Vacuum), with a typical operating pressure (under load) of
$1\times 10^{-5}$ mbar. The detection chamber provides a $\mu$-metal shielded
field-free flight tube for electrons or ions of 400 mm, before particles are
detected on microchannel plate (MCP) detector, coupled to a fast phosphor
screen (Photonis ADP 2PS, 40 mm, 1:60, P47 phosphor). This chamber is pumped
by a 700 l/s pump (HiPace 700, Pfeiffer Vacuum), with typical operating
pressures under load of $<1\times 10^{-6}$ mbar.

Sample molecules are introduced into the spectrometer by means of supersonic
expansion from a handmade glass nozzle. The nozzle tip has an opening diameter
of about 15 $\mu$m and is coated in silver to allow high voltages to be
applied. The nozzle is placed equidistant between the extractor and repeller
electrode of the VMI setup. A high voltage is applied to the silver coated
nozzle tip to minimize distortions of the electric field due to the tip, and
it is typically operated at the average voltage of repeller and extractor (i.
e., $V_{\text{tip}}=\frac{1}{2}(V_{\text{rep}}+V_{\text{extr}})$). The tip of
the nozzle is located $\sim 5$ mm from the interaction point in the center of
the VMI, where the expanding gas plume is intersected orthogonally by a
focussed laser beam. The VMI is based on the classic 3-plate design from
Eppink & Parker Eppink and Parker (1997), with an outer diameter of 40 mm, and
central orifices of 5 mm (extractor) and 10 mm (ground).

Produced charged particles are accelerated towards the detector, where we
either collect time-of-flight mass spectra or photoelectron images. For the
former, the response from the MCP is counted after pre-amplification and
constant-fraction-discrimination (Surface Concept 1-Channel Preamplifier-CFD)
using a high-resolution time-to-digital card (chronologic xTDC4-PCIe, 16 ps
bins). For electron detection, impact positions on the phosphor screen are
recorded with a fast CMOS camera (Basler acA720-520um), operating at a frame
rate of around 1 kHz and a resolution of $256\times 256$ pixel. To increase
resolution and overcome any spatial inhomogeneities of the detector response,
events are centroided on-the-fly and only centroids retained for further
analysis.

As a light source a 3 kHz Ti:Sapphire laser (Spectra Physics SpitfireAce) was
used, producing fundamental output centered at 800 nm and with 100 fs pulse
duration. Photons at 400 nm were generated by frequency doubling in a beta-
barium borate (BBO) crystal. The pulse energy was controlled by an attenuator
consisting of a half-wave plate (CASIX WPZ1315) and a linear Glan laser
polarizer. Circular polarized light was then generated by an achromatic
quarter-wave plate (B. Halle 300-470 nm). The produced helicity could be
inverted by rotating the waveplate 90 degrees using a motorized rotation stage
(Standa 8MRU-1). Both circular polarization states were characterized by
rotating a linear polarizer (Thorlabs GL10B) behind the quarterwave-plate
while measuring the transmitted power. Stokes vectors of (1, 0.06, 0.034,
-0.998) and (1, 0.053, 0.039, 0.998) were determined for LCP and RCP,
respectively (see supplementary information for further details). The laser is
focussed into the VMI spectrometer using a plano-spherical lens (Thorlabs
LA4904-UV, f = 15 cm), yielding typical spotsizes of 15 µm (FWHM).

A single PECD measurement consisted of 30000 camera frames for each of the two
polarizations, and hence takes about 1 min. For the data presented here, the
mean PECD and corresponding standard errors were determined from a total of
260 measurements, confidence intervals are given as two standard errors.
During data acquisition the polarization state sequence is alternated to
prevent systematic errors by long-term drifts, i.e. data is collected in the
sequence (LCP-RCP)-(RCP-LCP)-…. Photoelectron images were analyzed using
Python scripts including the pyAbel package Hickstein et al. (2016), utilizing
the rBasex method for inversion with finite differences regularization
(Strength = 30000) Gibson et al. (2022).

Samples of (S)- and (R)-Phenylethanol, each with a nominal purity of 97%, were
purchased from Merck Life Science and used without further purification. The
sample vapour (at room temperature) is picked up and transported to the nozzle
by a small Helium flow (300 mbar) for expansion into the vacuum chamber of the
spectrometer.

## Results and Discussion

An exemplary 400 nm multiphoton ionization mass spectrum of R-1-Phenylethanol
is shown in the top half of Figure 3. The inset shows a detailed view of the
m/z region $120-124$, which yields an experimental mass resolution (FWHM) of
$\frac{M}{\Delta M}=523$. The mass spectrum is dominated by the parent ion at
122 Da, with main fragments appearing at m/z = 43, 77, 78, 79, and 107. For
comparison, we also show a literature (NIST chemistry webbook) electron impact
ionization mass spectrum in the lower half of Figure 3 Wallace (2017). Its is
clear that femtosecond multiphoton ionization (fs-MPI) is a relatively soft
process, inducing significantly less fragmentation than electron impact
ionization, and a much larger fraction of intact parent ions Dauletyarov et
al. (2023). Apart from a small contamination appearing at m/z = 32, similar
fragments are encountered in both spectra, allowing the clear identification
of 1-Phenylethanol.

Figure 3: Mass spectra of R-1-Phenylethanol. (a) Time-of-flight mass spectrum
acquired in our new spectrometer using femtosecond multiphoton ionization at
400 nm. The inset shows in detail the mass region of the parent ion,
indicating the achievable mass resolution of our spectrometer. (b) Reference
electron impact ionization mass spectrum from the NIST database Wallace
(2017).

We now consider the photoelectron image and spectrum of 1-PE collected at 400
nm and shown in Figure 4, collected at laser pulse energies of 1.5 µJ
(corresponding to $\sim 2\times 10^{12}$ W/cm2). The photoelectron images
exhibited two features; a sharp and intense feature at very low electron
kinetic energy (eKE) in the center of the image, and a weaker outer ring at
higher eKE. The latter feature peaks at around 0.35 eV electron kinetic
energy. The (non-adiabatic) ionization energy for 1-PE has been reported as
8.89 eV Mons et al. (2000), ionization at 400 nm thus requires 3 photons which
carry a total photon energy of 9.3 eV. Assuming such a 3-photon process, our
data yields a vertical (non-adiabatic) ionization energy of $8.95\pm 0.1$ eV,
fully consistent with the literature value. The observed feature is
significantly broader than our experimental resolution and bandwidth (see S.I.
for details), most likely indicating a significant change in molecular
geometry upon photoionization, as has been noted before Mons et al. (2000).
From the high-energy cutoff at around 0.6 eV in Figure 4 we can estimate the
adiabatic ionization energy as around 8.7 eV.

Figure 4: Photoelectron spectrum and raw photoelectron image (inset) of
1-Phenylethanol at reduced laser power to avoid saturation effects in the
center of the image.

To obtain enantiosensitive information, photoelectron images were obtained for
both helicities of circularly polarized light. For these measurements we focus
only on the weaker outer feature, since a feature in the center of the image
does not yield information on the photoelectron angular distribution and hence
photoemission asymmetry. The used laser pulse energy was hence increased to 2
µJ (fluence of $\sim 3\times 10^{12}$ W/cm2) to obtain sufficient count rates
in the high eKE region. This caused centroiding artifacts in the image center
due to overlapping events and this region has therefore been masked out in the
images shown here.

Photoelectron images for both enantiomers and both helicities are shown in
Figure 5, where the left half always corresponds to the antisymmetric
difference image, and the right half to the symmetric sum image of the two
enantiomers. The top panels (a,b) show raw images, whereas the bottom panels
(c,d) show Abel-inverted images. The center of all images has been masked out,
as explained above.

Figure 5: Antisymmetric difference (left) and symmetric sum (right)
photoelectron images based on raw (a, b) as well as Abel-inverted data (c, d)
for S- (a, c) and R-1PE (b, d), respectively. For enhanced contrast the raw
images where $2\times$ binned, resulting in $128\times 128$ pixel images. The
central region was masked due to artifacts caused by abundant low kinetic
energy electrons. Dashed circles indicate the radial region used for
calculation of the PECD effect, see text for details. The laser propagation
direction is upwards in all images.

The anti-symmetrized difference images (LCP-RCP) for R- and S-1PE show a clear
forward-backward asymmetry along the laser propagation axis, with
photoelectrons for R-1PE preferentially emitted in the forward direction for
LCP light, and correspondingly in the backward direction for S-1PE. This is
apparent both in the raw difference images as well as in the Abel-inverted
images. This PECD effect can be quantized in a single value by either simple
hemispherical integration or based on the Legendre coefficients of the
photoelectron angular distributions. For this only the radial region of the
images containing the high-eKE features was used, as indicated by the dashed
yellow circles in Figure 5. The range used was 0.21–0.49 eV, corresponding to
the FWHM of the high eKE feature. For the hemispherical integration approach,
the PECD is then calculated from the respective electron yields in the forward
(_fwd_) and backward (_bwd_) direction for both helicities of light
(_rcp,lcp_) Janssen and Powis (2014); Lehmann et al. (2013):

$\mathrm{PECD}=2\left(\frac{Y_{lcp}^{fwd}-Y_{lcp}^{bwd}}{Y_{lcp}^{fwd}+Y_{lcp}^{bwd}}-\frac{Y_{rcp}^{fwd}-Y_{rcp}^{bwd}}{Y_{rcp}^{fwd}+Y_{rcp}^{bwd}}\right).$
(1)

This approach yielded PECD values of $+1.67\%\pm 0.26\%$ and $-2.25\%\pm
0.26\%$ for R- and S-1PE, respectively. Throughout, all given uncertainties
correspond to 2 standard errors.

Alternatively, we can define the PECD based on the (odd) Legendre coefficients
($\beta_{n}$) contributing to the Abel-inverted photoelectron image Cooper and
Zare (2003). For a 3-photon process Legendre polynomials up to order $n=5$
need to be taken into account, and hence we define the PECD as Lehmann et al.
(2013):

$\mathrm{PECD}=2\beta_{1}-\frac{1}{2}\beta_{3}+\frac{1}{4}\beta_{5}.$ (2)

This yielded PECD values of $+1.63\%\pm 0.30\%$ and $-2.08\%\pm 0.28\%$ for R-
and S-1PE, respectively.

Table 1: PECD values of enantiopure 1-PE samples determined by hemispherical integration of raw images and PAD fitting of Abel-inverted images. Uncertainties are given as $2\sigma$. Sample | PECD (Abel inversion) | PECD (hemisph. int.)
---|---|---
R-1PE | $+1.63\pm 0.30$ | $+1.67\pm 0.26$
S-1PE | $-2.08\pm 0.28$ | $-2.25\pm 0.26$

The corresponding values for the two enantiomers are also summarized in Table
1. PECD values extracted from both analysis frameworks are in excellent
agreement and show the expected sign inversion. They furthermore agree with a
recent PEELD study on chiral alcohols Greenwood and Williams (2023). The S-1PE
enantiomer consistently exhibited a larger PECD effect then measured for the
R-1PE, potentially indicating that our sample of R-1PE had a reduced
enantiopurity. This direct comparison of the two analysis pathways shows that
for reliable quantitative PECD determination a full Abel analysis of the VMI
images is not required (at least as long as there are no strongly overlapping
features), and the simpler hemispherical integration is sufficient. Indeed the
slightly larger uncertainties for Abel inversion are likely due to the reduced
number of electrons taken into account when only considering the central slice
of the distribution. The use of the VMI technique, and hence the ability to
define electron kinetic energy ranges over which to determine the PECD, is
highly beneficial compared to simple integrating detectors such as half-moon
anodes or electron multipliers. It allows differentiation of different
features in the photoelectron spectrum that frequently correspond to different
electronic states in the cation, with potentially very different PECD effects
that cannot be distinguished in simple integrating detectors. Indeed, this is
also the case for 1PE here. Abel inverted images in Figure 5 clearly show a
feature with inverted PECD asymmetry at lower radii, albeit with much reduced
intensity. Nonetheless, a simple integrating detector would have recorded much
lower PECD values of 1.00 (R-1PE) and -1.42 (S-1PE).

Abel analysis furthermore lets us analyze the contributions from individual
Legendre coefficients to the overall observed PAD. The PECD effect is
contained only in the odd Legendre coefficients, and the extracted values for
the two enantiomers are shown in Figure 6. Numerical values for all extracted
coefficients are given in the supplementary information. This clearly shows
that the main contributors to the PECD are the $\beta_{1}$ and $\beta_{3}$
terms, which also show the expected mirroring behavior.

Figure 6: Amplitudes of the odd Legendre coefficients for both enantiomers
extracted from the PAD fitting. The main contribution to the PECD comes from
the $\beta_{1}$ and $\beta_{3}$ terms, with all orders showing the expected
sign inversion between the enantiomers.

While the magnitude of the PECD effect of only $2~{}\%$ is small compared to
many previously investigated substances, it is clearly measurable with high
fidelity in our compact spectrometer. The extracted values are furthermore
comparable with a recently published photoelectron elliptical dichroism
(PEELD) study, which utilized (3+1) multiphoton ionization at 520 nm to study
(amongst others) chiral alcohols Greenwood and Williams (2023). A previous
study already demonstrated the recording of PECD signals in the region of
above-threshold ionization (ATI) and tunnel ionization Beaulieu et al. (2016),
with the current study we are within the multiphoton ionization region
(Keldysh parameter $\gamma\sim 2$). This confirms the earlier observation that
even with strong-field ionization, whether it is ATI, tunneling or MPI, the
PECD effect still persists.

## Conclusion

We have shown the first PECD measurements of 1-Phenylethanol using femtosecond
multiphoton ionization. This highlights the universality of the fs-MPI
approach for chiral analysis. The PECD for 1-PE, albeit small at only $\sim
2$%, was reliably extracted using both hemispherical integration and Abel
analysis, and showed the expect mirroring behavior. Experiments were conducted
on a new compact PECD spectrometer, featuring a small diameter nozzle and
optimized pumping geometry, which enables a small footprint mobile machine,
with much reduced pumping requirements compared to conventional gas-phase
photoelectron spectrometers. The use of VMI detection yields additional
spectroscopic information on the system and allows the filtering out of
particular photoelectron features for reliable PECD determination. In the
current iteration using a 3 kHz laser, reliable quantitative PECD analysis can
be performed in around one hour. We are currently working on implementing a
high repetition-rate femtosecond laser that will improve the duty cycle, and
hence reduce the measurement time, by 2–3 orders of magnitude. This will pave
the way for integration of PECD-based chiral analysis with standard analytical
separation techniques such as chromatography.

## Acknowledgements

This work was supported by the Netherlands Organization for Scientific
Research (NWO) under grant numbers STU.019.009, VIDI.193.037 and 712.018.004,
and the European Regional Development fund (EFRO, OP Oost) under project
number PROJ-00949. We furthermore thank the Spectroscopy of Cold Molecules
Department, and in particular Prof. Bas van de Meerakker, for continued
support.

## Conflicts of Interest

MHMJ is founder and CEO of _MassSpecpecD BV_ (www.MassSpecpecD.com).

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